Causality: The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors.Adverse Drug Reaction Reporting Systems: Systems developed for collecting reports from government agencies, manufacturers, hospitals, physicians, and other sources on adverse drug reactions.Drug-Related Side Effects and Adverse Reactions: Disorders that result from the intended use of PHARMACEUTICAL PREPARATIONS. Included in this heading are a broad variety of chemically-induced adverse conditions due to toxicity, DRUG INTERACTIONS, and metabolic effects of pharmaceuticals.Pharmacovigilance: The detection of long and short term side effects of conventional and traditional medicines through research, data mining, monitoring, and evaluation of healthcare information obtained from healthcare providers and patients.Nerve Net: A meshlike structure composed of interconnecting nerve cells that are separated at the synaptic junction or joined to one another by cytoplasmic processes. In invertebrates, for example, the nerve net allows nerve impulses to spread over a wide area of the net because synapses can pass information in any direction.Mendelian Randomization Analysis: The use of the GENETIC VARIATION of known functions or phenotypes to correlate the causal effects of those functions or phenotypes with a disease outcome.Drug-Induced Liver Injury: A spectrum of clinical liver diseases ranging from mild biochemical abnormalities to ACUTE LIVER FAILURE, caused by drugs, drug metabolites, and chemicals from the environment.Information Theory: An interdisciplinary study dealing with the transmission of messages or signals, or the communication of information. Information theory does not directly deal with meaning or content, but with physical representations that have meaning or content. It overlaps considerably with communication theory and CYBERNETICS.Models, Neurological: Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.Brain Mapping: Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.Computer Simulation: Computer-based representation of physical systems and phenomena such as chemical processes.Epidemiology: Field of medicine concerned with the determination of causes, incidence, and characteristic behavior of disease outbreaks affecting human populations. It includes the interrelationships of host, agent, and environment as related to the distribution and control of disease.Neurophysiology: The scientific discipline concerned with the physiology of the nervous system.Expert Testimony: Presentation of pertinent data by one with special skill or knowledge representing mastery of a particular subject.Neural Networks (Computer): A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.Neural Pathways: Neural tracts connecting one part of the nervous system with another.Algorithms: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.Magnetic Resonance Imaging: Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.Risk Factors: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.Brain: The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.Nonlinear Dynamics: The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.Data Interpretation, Statistical: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.Signal Processing, Computer-Assisted: Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.Plant Preparations: Material prepared from plants.Republic of BelarusPerception: The process by which the nature and meaning of sensory stimuli are recognized and interpreted.Linear Models: Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.Electroencephalography: Recording of electric currents developed in the brain by means of electrodes applied to the scalp, to the surface of the brain, or placed within the substance of the brain.Magnetoencephalography: The measurement of magnetic fields over the head generated by electric currents in the brain. As in any electrical conductor, electric fields in the brain are accompanied by orthogonal magnetic fields. The measurement of these fields provides information about the localization of brain activity which is complementary to that provided by ELECTROENCEPHALOGRAPHY. Magnetoencephalography may be used alone or together with electroencephalography, for measurement of spontaneous or evoked activity, and for research or clinical purposes.Time Factors: Elements of limited time intervals, contributing to particular results or situations.Models, Statistical: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.Confounding Factors (Epidemiology): Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.Cognition: Intellectual or mental process whereby an organism obtains knowledge.Evoked Potentials: Electrical responses recorded from nerve, muscle, SENSORY RECEPTOR, or area of the CENTRAL NERVOUS SYSTEM following stimulation. They range from less than a microvolt to several microvolts. The evoked potential can be auditory (EVOKED POTENTIALS, AUDITORY), somatosensory (EVOKED POTENTIALS, SOMATOSENSORY), visual (EVOKED POTENTIALS, VISUAL), or motor (EVOKED POTENTIALS, MOTOR), or other modalities that have been reported.Cross-Sectional Studies: Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.Statistics as Topic: The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.Gene Regulatory Networks: Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations.Prospective Studies: Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.Longitudinal Studies: Studies in which variables relating to an individual or group of individuals are assessed over a period of time.Psychomotor Performance: The coordination of a sensory or ideational (cognitive) process and a motor activity.Image Processing, Computer-Assisted: A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.Perceptual Disorders: Cognitive disorders characterized by an impaired ability to perceive the nature of objects or concepts through use of the sense organs. These include spatial neglect syndromes, where an individual does not attend to visual, auditory, or sensory stimuli presented from one side of the body.Likelihood Functions: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.Cohort Studies: Studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.Epidemiologic Studies: Studies designed to examine associations, commonly, hypothesized causal relations. They are usually concerned with identifying or measuring the effects of risk factors or exposures. The common types of analytic study are CASE-CONTROL STUDIES; COHORT STUDIES; and CROSS-SECTIONAL STUDIES.Case-Control Studies: Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.Macaca: A genus of the subfamily CERCOPITHECINAE, family CERCOPITHECIDAE, consisting of 16 species inhabiting forests of Africa, Asia, and the islands of Borneo, Philippines, and Celebes.Photic Stimulation: Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.Odds Ratio: The ratio of two odds. The exposure-odds ratio for case control data is the ratio of the odds in favor of exposure among cases to the odds in favor of exposure among noncases. The disease-odds ratio for a cohort or cross section is the ratio of the odds in favor of disease among the exposed to the odds in favor of disease among the unexposed. The prevalence-odds ratio refers to an odds ratio derived cross-sectionally from studies of prevalent cases.Incidence: The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases, new or old, in the population at a given time.Genetic Predisposition to Disease: A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.Risk Assessment: The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988)Regression Analysis: Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.Data Mining: Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.Image Interpretation, Computer-Assisted: Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.Prevalence: The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.Age Factors: Age as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or the effect of a circumstance. It is used with human or animal concepts but should be differentiated from AGING, a physiological process, and TIME FACTORS which refers only to the passage of time.Models, Theoretical: Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.Epidemiologic Methods: Research techniques that focus on study designs and data gathering methods in human and animal populations.Databases, Factual: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.Environmental Exposure: The exposure to potentially harmful chemical, physical, or biological agents in the environment or to environmental factors that may include ionizing radiation, pathogenic organisms, or toxic chemicals.Rest: Freedom from activity.Visual Perception: The selecting and organizing of visual stimuli based on the individual's past experience.Pregnancy: The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.Questionnaires: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.United StatesSeverity of Illness Index: Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.Phenotype: The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.Reproducibility of Results: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.Prefrontal Cortex: The rostral part of the frontal lobe, bounded by the inferior precentral fissure in humans, which receives projection fibers from the MEDIODORSAL NUCLEUS OF THE THALAMUS. The prefrontal cortex receives afferent fibers from numerous structures of the DIENCEPHALON; MESENCEPHALON; and LIMBIC SYSTEM as well as cortical afferents of visual, auditory, and somatic origin.Bayes Theorem: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.Breast Feeding: The nursing of an infant at the breast.Multivariate Analysis: A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.Neoplasms: New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.Sex Factors: Maleness or femaleness as a constituent element or influence contributing to the production of a result. It may be applicable to the cause or effect of a circumstance. It is used with human or animal concepts but should be differentiated from SEX CHARACTERISTICS, anatomical or physiological manifestations of sex, and from SEX DISTRIBUTION, the number of males and females in given circumstances.Smoking: Inhaling and exhaling the smoke of burning TOBACCO.Air Pollution: The presence of contaminants or pollutant substances in the air (AIR POLLUTANTS) that interfere with human health or welfare, or produce other harmful environmental effects. The substances may include GASES; PARTICULATE MATTER; or volatile ORGANIC CHEMICALS.Cerebral Cortex: The thin layer of GRAY MATTER on the surface of the CEREBRAL HEMISPHERES that develops from the TELENCEPHALON and folds into gyri and sulchi. It reaches its highest development in humans and is responsible for intellectual faculties and higher mental functions.Reaction Time: The time from the onset of a stimulus until a response is observed.Functional Laterality: Behavioral manifestations of cerebral dominance in which there is preferential use and superior functioning of either the left or the right side, as in the preferred use of the right hand or right foot.Models, Biological: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.Logistic Models: Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor.Infant, Newborn: An infant during the first month after birth.Polymorphism, Single Nucleotide: A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.Stress, Psychological: Stress wherein emotional factors predominate.Depression: Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.Genotype: The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.Models, Genetic: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.Epilepsy: A disorder characterized by recurrent episodes of paroxysmal brain dysfunction due to a sudden, disorderly, and excessive neuronal discharge. Epilepsy classification systems are generally based upon: (1) clinical features of the seizure episodes (e.g., motor seizure), (2) etiology (e.g., post-traumatic), (3) anatomic site of seizure origin (e.g., frontal lobe seizure), (4) tendency to spread to other structures in the brain, and (5) temporal patterns (e.g., nocturnal epilepsy). (From Adams et al., Principles of Neurology, 6th ed, p313)Retrospective Studies: Studies used to test etiologic hypotheses in which inferences about an exposure to putative causal factors are derived from data relating to characteristics of persons under study or to events or experiences in their past. The essential feature is that some of the persons under study have the disease or outcome of interest and their characteristics are compared with those of unaffected persons.Gene Expression Profiling: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.Quantitative Trait Loci: Genetic loci associated with a QUANTITATIVE TRAIT.Space Perception: The awareness of the spatial properties of objects; includes physical space.Alcohol Drinking: Behaviors associated with the ingesting of alcoholic beverages, including social drinking.Residence Characteristics: Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.Follow-Up Studies: Studies in which individuals or populations are followed to assess the outcome of exposures, procedures, or effects of a characteristic, e.g., occurrence of disease.

Osteopenia in the patient with cancer. (1/1739)

Osteopenia is defined as a reduction in bone mass. It is commonly known to occur in elderly people or women who are postmenopausal due to hormonal imbalances. This condition, however, can result because of many other factors, such as poor nutrition, prolonged pharmacological intervention, disease, and decreased mobility. Because patients with cancer experience many of these factors, they are often predisposed to osteopenia. Currently, patients with cancer are living longer and leading more fulfilling lives after treatment. Therefore, it is imperative that therapists who are responsible for these patients understand the risk factors for osteopenia and their relevance to a patient with cancer.  (+info)

Onchocerciasis and epilepsy: a matched case-control study in the Central African Republic. (2/1739)

The occurrence of epileptic seizures during onchocercal infestation has been suspected. Epidemiologic studies are necessary to confirm the relation between onchocerciasis and epilepsy. A matched case-control study was conducted in dispensaries of three northwestern towns of the Central African Republic. Each epileptic case was matched against two nonepileptic controls on the six criteria of sex, age (+/-5 years), residence, treatment with ivermectin, date of last ivermectin dose, and the number of ivermectin doses. Onchocerciasis was defined as at least one microfilaria observed in iliac crest skin snip biopsy. A total of 561 subjects (187 cases and 374 controls) were included in the study. Of the epileptics, 39.6% had onchocerciasis, as did 35.8% of the controls. The mean dermal microfilarial load was 26 microfilariae per mg of skin (standard deviation, 42) in the epileptics and 24 microfilariae per mg of skin (standard deviation, 48) in the controls. This matched case-control study found some relation (odds ratio = 1.21, 95% confidence interval 0.81-1.80), although it was nonstatistically significant.  (+info)

Evaluation of the quality of an injury surveillance system. (3/1739)

The sensitivity, positive predictive value, and representativeness of the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) were assessed. Sensitivity was estimated at four centers in June through August 1992, by matching independently identified injuries with those in the CHIRPP database. The positive predictive value was determined by reviewing all "injuries" in the database (at Montreal Children's Hospital) that could not be matched. Representativeness was assessed by comparing missed with captured injuries (at Montreal Children's Hospital) on demographic, social, and clinical factors. Sensitivity ranged from 30% to 91%, and the positive predictive value was 99.9% (i.e., the frequency of false-positive capture was negligible). The representativeness study compared 277 missed injuries with 2,746 captured injuries. The groups were similar on age, sex, socioeconomic status, delay before presentation, month, and day of presentation. Injuries resulting in admissions, poisonings, and those presenting overnight were, however, more likely to be missed. The adjusted odds ratio of being missed by CHIRPP for admitted injuries (compared with those treated and released) was 13.07 (95% confidence interval 7.82-21.82); for poisonings (compared with all other injuries), it was 9.91 (95% confidence interval 5.39-18.20); and for injuries presenting overnight (compared with those presenting during the day or evening), it was 4.11 (95% confidence interval 3.11-5.44). These injuries were probably missed because of inadequate education of participants in the system. The authors conclude that CHIRPP data are of relatively high quality and may be used, with caution, for research and public health policy.  (+info)

Power and sample size calculations in case-control studies of gene-environment interactions: comments on different approaches. (4/1739)

Power and sample size considerations are critical for the design of epidemiologic studies of gene-environment interactions. Hwang et al. (Am J Epidemiol 1994;140:1029-37) and Foppa and Spiegelman (Am J Epidemiol 1997;146:596-604) have presented power and sample size calculations for case-control studies of gene-environment interactions. Comparisons of calculations using these approaches and an approach for general multivariate regression models for the odds ratio previously published by Lubin and Gail (Am J Epidemiol 1990; 131:552-66) have revealed substantial differences under some scenarios. These differences are the result of a highly restrictive characterization of the null hypothesis in Hwang et al. and Foppa and Spiegelman, which results in an underestimation of sample size and overestimation of power for the test of a gene-environment interaction. A computer program to perform sample size and power calculations to detect additive or multiplicative models of gene-environment interactions using the Lubin and Gail approach will be available free of charge in the near future from the National Cancer Institute.  (+info)

An IgG1 titre to the F1 and V antigens correlates with protection against plague in the mouse model. (5/1739)

The objective of this study was to identify an immunological correlate of protection for a two-component subunit vaccine for plague, using a mouse model. The components of the vaccine are the F1 and V antigens of the plague-causing organism, Yersinia pestis, which are coadsorbed to alhydrogel and administered intramuscularly. The optimum molar ratio of the subunits was determined by keeping the dose-level of either subunit constant whilst varying the other and observing the effect on specific antibody titre. A two-fold molar excess of F1 to V, achieved by immunizing with 10 micrograms of each antigen, resulted in optimum antibody titres. The dose of vaccine required to protect against an upper and lower subcutaneous challenge with Y. pestis was determined by administering doses in the range 10 micrograms F1 + 10 micrograms V to 0.01 microgram F1 + 0.01 microgram V in a two-dose regimen. For animals immunized at the 1-microgram dose level or higher with F1 + V, an increase in specific IgG1 titre was observed over the 8 months post-boost and they were fully protected against a subcutaneous challenge with 10(5) colony-forming units (CFU) virulent Y. pestis at this time point. However, immunization with 5 micrograms or more of each subunit was required to achieve protection against challenge with 10(7) CFU Y. pestis. A new finding of this study is that the combined titre of the IgG1 subclass, developed to F1 plus V, correlated significantly (P < 0.05) with protection. The titres of IgG1 in vaccinated mice which correlated with 90%, 50% and 10% protection have been determined and provide a useful model to predict vaccine efficacy in man.  (+info)

Is perforation of the appendix a risk factor for tubal infertility and ectopic pregnancy? An appraisal of the evidence. (6/1739)

OBJECTIVE: To critically assess the evidence that appendiceal perforation is a risk factor for subsequent tubal infertility or ectopic pregnancy. DATA SOURCES: Epidemiologic studies investigating the relationship between appendectomy and infertility or ectopic pregnancy were identified by searching the MEDLINE database from 1966 to 1997. Appropriate citations were also extracted from a manual search of the bibliographies of selected papers. STUDY SELECTION: Twenty-three articles were retrieved. Only 4 presented original data including comparisons to a nonexposed control group and they form the basis for this study. DATA EXTRACTION: Because the raw data or specific techniques of data analysis were not always explicitly described, indices of risk for exposure were extracted from the data as presented and were analysed without attempting to convert them to a common measure. DATA SYNTHESIS: Articles were assessed according to the criteria of the Evidence-Based Medicine Working Group for evaluating articles on harm. Review of the literature yielded estimates of the risk of adverse fertility outcomes ranging from 1.6 (95% confidence interval [CI] 1.1 to 2.5) for ectopic pregnancy after an appendectomy to 4.8 (95% CI 1.5 to 14.9) for tubal infertility from perforation of the appendix. Recall bias, and poor adjustment for confounding variables in some reports, weakened the validity of the studies. CONCLUSIONS: The methodologic weaknesses of the studies do not permit acceptance of increased risk of tubal pregnancy or infertility as a consequence of perforation of the appendix, so a causal relationship cannot be supported by the data currently available. Only a well-designed case-control study with unbiased ascertainment of exposure and adjustment for confounding variables will provide a definitive answer.  (+info)

Assessing public health capacity to support community-based heart health promotion: the Canadian Heart Health Initiative, Ontario Project (CHHIOP). (7/1739)

This paper presents initial findings of the Canadian Heart Health Initiative, Ontario Project (CHHIOP). CHHIOP has two primary objectives. The programmatic objective is to coordinate and refine a system for supporting effective, sustained community-based heart health activities. This paper addresses the scientific objective: to develop knowledge of factors that influence the development of predisposition and capacity to undertake community-based heart health activities in public health departments. A systems theory framework for an ecological approach to health promotion informs the conceptualization of the key constructs, measured using a two-stage longitudinal design which combines quantitative and qualitative methods. This paper reports the results of the first round of quantitative survey data collected from all health departments in Ontario (N = 42) and individuals within each health department involved in heart health promotion (n = 262). Results indicate low levels of implementation of heart health activities, both overall and for particular risk factors and settings. Levels of capacity are also generally low, yet predisposition to undertake heart health promotion activities is reportedly high. Analyses show that implementation is positively related to capacity but not predisposition, while predisposition and capacity are positively related. Overall, results suggest predisposition is a necessary but not sufficient condition for implementation to occur; capacity-related factors appear to be the primary constraint. These findings are used to inform strategies to address CHHIOP's programmatic objective.  (+info)

Immunologic parameters as predictive factors of cytomegalovirus disease in renal allograft recipients. (8/1739)

Cytomegalovirus (CMV) disease is a major problem in renal transplant recipients, but few predictive markers of the disease are known. Several immunologic parameters of potential relevance for the defense against CMV were measured after renal transplantation in 25 patients before any manifestations of CMV infection occurred. In 10 patients who later developed CMV disease, plasma levels of interleukin-8 were significantly higher, whereas the levels of macrophage inflammatory protein-1alpha (MIP-1alpha) were significantly lower than in 15 patients who did not develop CMV disease. Also, lower numbers of CD4+ and CD8+ lymphocytes were observed in patients who later had CMV disease. These findings were independent of previous rejection therapy and were particularly pronounced in patients with primary CMV infection. Interleukin-8 and MIP-1alpha may be predictive markers of CMV disease and could be of potential use in selecting patients for prophylactic treatment.  (+info)

Industrial processes are often subjected to abnormal events such as faults or external disturbances which can easily propagate via the process units. Establishing causal dependencies among process measurements has a key role in fault diagnosis due to its ability to identify the root cause of a fault and its propagation path. This paper proposes a hybrid nonlinear causal analysis based on nonparametric multiplicative regression (NPMR) for identifying the propagation of an oscillatory disturbance via control loops. The NPMR causality estimator addresses most of the limitations of the linear model-based methods and it can be applied to both bivariate and multivariate estimations without any modifications to the method parameters. Moreover, the NPMR-based estimations can be used to pinpoint the root cause of a fault. The process connectivity information is automatically integrated into the causal analysis using a specialized search algorithm. Thereby, it enables to efficiently tackle industrial ...
Background. Our previous studies have implicated the primary visual cortex (V1) as the putative visuo-spatial sketchpad for working-memory, but in a supramodal form. To establish this memory-related role for V1, we need to determine the source of its top-down modulation from higher-order memory mechanisms, including medial-temporal lobe (MTL) structures such as the hippocampus and perirhinal cortex (PRC) (Likova, 2012, 2013), which has direct anatomical connection to V1 (Clavagnier et al., 2004). Indeed, V1 and the hippocampus exhibited correlated changes under a memory-based training intervention (Likova, 2015); moreover, the representations for both memory and perception were confirmed as supramodal in PRC (Cacciamani & Likova, 2016). Now, to address the key question of the direction and significance of influence between these memory areas and V1, we ran Granger Causality analysis. Methods. Using fMRI in blind subjects before and after a unique memory-guided drawing intervention ...
It has been proposed that the Grading of Recommendations Assessment, Development and Evaluation (GRADE) for public health questions should consider the Bradford Hill criteria for causation and that GRADE requires adaptation.1 In this article, we describe the relation of the Bradford Hill criteria to the GRADE approach to grading the quality of evidence and strength of recommendations. The primary concern seems that evidence from non-randomised studies may provide a more adequate or best available measure of a public health strategys impact, but that such evidence might be graded as lower quality in the GRADE framework. We would like to reiterate that GRADE presents a framework that describes both criteria for assessing the quality of research evidence and the strength of recommendations. In assessing quality of evidence, GRADE notes that randomisation is only one of many relevant factors. Furthermore, GRADE is not specific to the narrow field of therapeutic interventions. Indeed, it likely is ...
Common methods of causal inference generate directed acyclic graphs (DAGs) that formalize causal relations between n variables. Given the joint distribution of all these variables, the DAG contains all information about how intervening on one variable would change the distribution of the other n-1 variables. It remains, however, a non-trivial question how to quantify the causal influence of one variable on another one.
Here we propose a measure for causal strength that refers to direct effects and measure the strength of an arrow or a set of arrows. It is based on a hypothetical intervention that modifies the joint distribution by cutting the corresponding edge. The causal strength is then the relative entropy distance between the old and the new distribution.
We discuss other measures of causal strength like the average causal effect, transfer entropy and information flow and describe their limitations. We argue that our measure is also more appropriate for time series than the known ones.
Public elementary and secondary school students; suspension and expulsion; sufficient cause. Provides that in no case shall sufficient cause for the suspension or expulsion of a student from attendance at a public elementary or secondary school include only instances of truancy or nonviolent behavior. Current law provides that in no cases may sufficient cause for suspensions include only instances of truancy.
From the perspective of causal diagrams, several studies had claimed that matching on confounders C in matched case-control designs can improve estimation precision for the effect of exposure (E) on outcome (D), though it fails to remove confounding effect of C [8, 9]. Therefore, further adjustment for C using conditional or unconditional logistic regression model after matching is widely used to eliminate the confounding bias of C in analytic epidemiology [13, 14]. When C is exactly a confounder for E and D (scenario 1, Fig. 1a), however, our simulation results did not illustrate distinct improvement of precision for estimating effect of E on D by matching on C (model 3) comparing with by non-matching designs (model 1). Nevertheless, the benefit of matching on C was to greatly reduce the bias for estimating the effect of E on D (model 3) though failed to completely remove the bias (Fig. 2a and b). Further adjusting for C using logistic regression model (model 4 or model 5) after matching almost ...
TY - JOUR. T1 - Causal diagrams and multivariate analysis I. T2 - A quiver full of arrows. AU - Jupiter, Daniel C.. PY - 2014/9. Y1 - 2014/9. N2 - How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways.. AB - How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways.. KW - Confounder. KW - Effect modification. KW - Multivariate analysis. KW - Precision variable. UR - http://www.scopus.com/inward/record.url?scp=84906789952&partnerID=8YFLogxK. UR - ...
The Causal Analysis/Diagnosis Decision Information System, or CADDIS, is designed to help scientists and engineers in the Regions, States, and Tribes conduct causal assessments in aquatic systems. It is organized into five volumes ...
From patterns to pathways. Causal analysis of gene expression data Alexander Kel BIOBASE GmbH Halchtersche Strasse 33 D-38304 Wolfenbuettel Germany
Downloadable! The factor of the earlier/later closing market, which appears in pairs of time series with non-synchronism problem exposure, may predetermine the results of the Granger causality test conducted on classic form. The shift in GMT timeline reverses the exposure of the market to the factor of earlier/later closing market, and may change the results of Granger causality test conducted on classic form. Verification of the given assumption on empirical data demonstrated that the US market, having moved from the later closing market to the earlier closing market condition (factor), started to show the behavior similar to other earlier closing markets.
Advanced Statistics Assignment Help, Causality, Causality: The relating of the reasons to the effects they produce. Several investigations in medicine seek to establish the causal relations between the events, for instance, which receiving the treatment A causes patients to live longer than takin
Contents: There are two reasons for a statistical analysis. One is prediction of future data based on what one has learned from past data and accounting for uncertainty. Prediction need not be concerned with understanding cause-effect relationships, but understanding causality is central to our understanding of data and how we use that knowledge. For instance, standard statistical techniques allow to predict the survival probability of a current smoker, typically predicting earlier death compared to non-smokers. But there is no standard statistical technique that analyses the causal effect of smoking on mortality. The difficulty is that smoking is not assigned in a randomized experiment, and there are more differences between smokers and non-smokers than just smoking status. In fact, defining a causal effect is not even part of the usual statistical and mathematical formalism. In the last 30 years or so, there has been a statistical revolution of developing causal inference, motivated by ...
On the regularity view of causality formulated by Hume, "a cause [is defined] to be an object followed by another, and where all the objects, similar to the first, are followed by objects similar to the second."26 In the medical sciences, this view of causality is obviously too simple and Mackies23 more subtle version of the regularity view seems more suitable.27-29. On Mackies account, a causal complex may be seen as a conjunction of factors that only jointly are sufficient for bringing about the effect. There may be several different causal complexes that are all sufficient for bringing about the effect; hence, none of them are necessary. A factor in such a causal complex is an insufficient but necessary condition in an unnecessary but sufficient causal complex, also called an INUS condition. This model has independently been applied to epidemiology as the component-cause model,28,29 and in accordance with standard epidemiological terminology we shall refer to INUS conditions as causal ...
Redundant causation from a sufficient cause perspective. Gatto, Nicolle M.; Campbell, Ulka B. // Epidemiologic Perspectives & Innovations;2010, Vol. 7, p5 Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any... ...
PubMed journal article The HFE Cys282Tyr mutation as a necessary but not sufficient cause of clinical hereditary hemochromatosi were found in PRIME PubMed. Download Prime PubMed App to iPhone or iPad.
Causality 3: Itt játszhatod a következőt Causality 3. - A Causality 3 a válogatott Mutass és kattints kalandjáték Játékok egyike.
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Downloadable (with restrictions)! Author(s): Michaud, Pierre-Carl & van Soest, Arthur. 2008 Abstract: A positive relationship between socio-economic status (SES) and health, the health-wealth gradient, is repeatedly found in many industrialized countries. This study analyzes competing explanations for this gradient: causal effects from health to wealth (health causation) and causal effects from wealth to health (wealth or social causation). Using six biennial waves of couples aged 51-61 in 1992 from the US Health and Retirement Study, we test for causality in panel data models incorporating unobserved heterogeneity and a lag structure supported by specification tests. In contrast to tests relying on models with only first order lags or without unobserved heterogeneity, these tests provide no evidence of causal wealth health effects. On the other hand, we find strong evidence of causal effects from both spouses health on household wealth. We also find an effect of the husbands health on the wifes
Schippers, Renken and Keysers (NeuroImage, 2011) present a simulation of multi-subject lag-based causality estimation. We fully agree that single-subject evaluations (e.g., Smith et al., 2011) need to be revisited in the context of multi-subject studies, and Schippers paper is a good example, including detailed multi-level simulation and cross-subject statistical modelling. The authors conclude that the average chance to find a significant Granger causality effect when no actual influence is present in the data stays well below the p-level imposed on the second level statistics and that when the analyses reveal a significant directed influence, this direction was accurate in the vast majority of the cases. Unfortunately, we believe that the general meaning that may be taken from these statements is not supported by the papers results, as there may in reality be a systematic (group-average) difference in haemodynamic delay between two brain areas. While many statements in the paper (e.g., the final
This chapter presents fundamental concepts related to risk assessment and causal inference in the health sciences. It discusses the processes involved in the identification of risk and causative...
Joint causal inference on observational and experimental data: https://arxiv.org/abs/1611.10351 NIPS 2016 What If? workshop - https://sites.google.com/site/w…
Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric meth-ods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statis-tics. He was awarded a Medallion lecture by the Institute of Mathematical Statistics in 2009 and read a paper to the Royal Statistical Society in 2010.. Abstract: Understanding cause-effect relationships between variables is of great interest in many fields of science. An ambitious but highly desirable goal is to infer causal effects from observational data obtained by observing a system of interest without subjecting it to interventions. This would allow to circumvent severe experimental constraints or to sub-stantially lower experimental costs. Our main motivation to study this goal comes from applications in biology.. We present recent progress for prediction of causal effects with direct implications on designing ...
Fans causal attributions for a game outcome refer to their assessments of the underlying reasons for why things turned out as they did. We investigate the extent to which team identification moderates fans attributional responses to a game outcome so as to produce a self-serving bias that favors the preferred team. Also explored is the ability of team identification to mediate the effect of attributions on the summary judgments of basking in reflected glory (BIRG) and satisfaction with the teams performance. Consistent with a self-serving bias, we found that highly identified fans were more likely to attribute a winning effort to stable and internal causes than were lowly identified fans. Moreover, the extremity of response between winners and losers was greater among highly identified fans than lowly identified fans. Team identification was also found to mediate the influence of (a) stability on BIRGing and (b) internal control on BIRGing. No such mediation effects were observed in the case ...
We outline the guiding ideas behind mechanisms-based theorizing in analytical sociology as a fruitful alternative to economics-inspired research on identification of causal effects, and discuss the potential of mechanisms-based theorizing for further development in organization and innovation studies. We discuss the realist stance on providing broader explanations as an identifying characteristic of the mechanism approach, its focus on the dynamic processes through which outcomes to be explained are brought about, and outline theoretical and methodological implications for organization and innovation studies.. ...
Relationship between two popular modeling frameworks of causalinference from observational data, namely, causal graphical model andpotential outcome causal model is discussed. How some popular causaleffect estimators found in applications of the potential outcome causalmodel, such as inverse probability of treatment weighted estimator anddoubly robust estimator can be obtained by using the causal graphicalmodel is shown. We confine to the simple case of binary outcome andtreatment variables with discrete confounders and it is shown how togeneralize results to cases of continuous variables.. ...
In my previous blogpost on the p-curve, I showed that the Granger causality tests we meta-analysed in our Energy Journal paper in 2014 form a right-skewed p-curve. This would mean that there was a "true effect" according to the p-curve methodology. However, our meta-regression analysis where we regressed the test statistics on the square root of degrees of freedom in the underlying regressions showed no "genuine effect". Now I understand what is going on. The large number of highly significant results in the Granger causality meta-dataset is generated by "overfitting bias". This result is "replicable". If we fit VAR models to more such short time series we will again get large numbers of significant results. However, regression analysis shows that this result is bogus as the p-values are not negatively correlated with degrees of freedom. Therefore, the power trace meta-regression is a superior method to the p-curve. In addition, we can modify this regression model to account for omitted ...
Whether and, if yes, to what extent the degree of an effect differs according to the values of Z depends, however, on the choice of the model and the associated index of effect magnitude. As mentioned above, some effect measures (e.g. the odds ratio) usually serve only to quantify the magnitude of a causal effect supposed to be constant between the individuals.. Moreover, the risk difference is the only measure for which effect heterogeneity is logically linked with causal co-action in terms of counterfactual effects. To explain this, it is necessary to define the causal synergy of two binary factors, X i and Z i (coded as 0 or 1), on a binary outcome Y i in an individual i (at fixed time).. Clearly, if X i and Z i do not act together in causing the event Y i = 1, then. (a) if Y i = 1 is caused by X i only,. Y i = 1 if (X i = 1 and Z i = 0) or. (X i = 1 and Z i = 1). and Y i = 0 in all other cases. Thus, Y i = 1 occurs in all cases where X i = 1 and in no other cases.. (b) if Y i = 1 is caused ...
NP is often treated with pharmacological drugs or other therapy in order to alleviate symptoms or target the causal mechanism of pain. However many treatments are still ineffective for a large percentage of those who suffer from NP. Recent research has identified various genes that confer protection or susceptibility to the development of NP. The identification of these genes and the study of the causal mechanisms of pain will allow the development of more effective treatment ...
從圖書館擷取資料! Adverse effects of vaccines : evidence and causality. [Kathleen R Stratton; Institute of Medicine (U.S.). Committee to Review Adverse Effects of Vaccines.]
David Albert (2000) and Barry Loewer (2007) have argued that the temporal asymmetry of our concept of causal influence or control is grounded in the statistical mechanical assumption of a low-entropy past. In this paper I critically examine Alberts and Loewers accounts.. ...
Anyone trying to comprehend the problems of "the environment" might well be bewildered by their number, variety and complication. There is a natural temptation to try to reduce them to simpler, more manageable elements, as with mathematical models and computer simulations. This, after all, has been the successful programme of Western science and technology up to now. But environmental problems have features which prevent reductionist approaches from having any, but the most limited useful effect. These are what we mean when we use the term "complexity".. Complexity is a property of certain sorts of systems; it distinguishes them from those which are simple, or merely complicated. Simple systems can be captured (in theory or in practice) by a deterministic, linear causal analysis. Such are the classic scientific explanations, notably those of high-prestige fields like mathematical physics. Sometimes such a system requires more variables for its explanation or control than can be neatly managed in ...
When I originally started looking into this last August, I sent an e-mail to the corresponding author asking for a couple of tables with information on "pre-treatment" differences between the exposure groups. I did not receive this. This is quite understandable, given that they were experiencing a media-blitz and most likely had their hands full. I therefore turned to past publications on the Dunedin cohort to see if I could find the relevant information there.. It turned out that I could - to some extent. Early onset cannabis use appeared to be correlated with a number of risk factors, and these risk factors were also correlated with poor life outcomes (low and poor education, crime, income etc.). The risk factors were also correlated with socioeconomic status.. The next question was whether these factors could affect IQ. One recent model of IQ (the Flynn-Dickens model) strongly suggested they would. The model sees IQ as a style or habit of thinking - a mental muscle, if you like - which is ...
Can causal models be evaluated? Isabelle Guyon ClopiNet / ChaLearn http://clopinet.com/causality [email protected] Acknowledgements and references. Feature Extraction, Foundations and Applications I. Guyon, S. Gunn, et al. Springer, 2006. http://clopinet.com/fextract-book Slideshow...
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.. ...
Critics dismiss some theistic arguments as "god of the gaps", however, in the case of these three "enduring gaps" -- great phrase, Doug! -- it is not what we dont know, it is what we do know. Everything that begins to exist must have a sufficient cause and reason -- and the only sufficient explanatory cause for "us" is God ...
Now, before I go on to describe what I think that causality IS, let me explain what I think it isnt. I think that there is a difference because influence and cause. A cause is something that is necessary to the chain of events - without that cause, the event would not happen. Influence is merely something that inclined things in a certain direction. For example, if I flick the light switch, I am causing the light to turn on. On the other hand, if I tell my sister that Im going to watch House, that may influence her to stop working on homework. You can see the difference. My sister would probably stop working on her homework even without my influence; my influence was not part of the necessary chain of a events and therefore was not a cause. Secondly, I do not think that causality is passive. If something is passive, it will simply allow things to keep on doing what they are already doing. You can actively push your younger brother, and you can actively block your older brother from pushing ...
In an observational study, the researcher identifies a condition or outcome of interest and then measures factors that may be related to that outcome. Although observational studies cannot lead to strong causal inferences, they may nonetheless suggest certain causal hypotheses. To infer causation in observational studies, investigators attempt to establish a sequence of events if event A generally precedes event B in time, then it is possible that A may be responsible for causing B. Such studies may be either (the investigator tries to reconstruct what happened in the past) or prospective (the investigator identifies a group of individuals and
... _1.30/TableLtfu,tblLTFU]]: Added [[Hicdep_1.30/TableLtfu/FieldDeathRc1,DEATH_RC#]] to code for causal relation of the [[Hicdep_1.30/TableLtfu/FieldDeathR1,DEATH_R#]] code to the death in order to comply with [[Hicdep_1.30/CoDe,CoDe]] and still maintain a format to be used for cohorts not using !CoDe. [[Hicdep_1.30/TableLtfu/FieldIcd10_1,ICD10 ...
Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as integrated information and causal density. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in
We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the...
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The psychological literature on causation includes a variety of proposed analyses of causal relations: simple correlation, mental models, causal powers, etc. Each of these proposals is subject to everyday counterexamples. Correlation accounts confuse intervention with conditioning and yield statistical jokes; mental models fail to capture conditional information; races and voting present difficulties for causal power theories, as do existing philosophical accounts of causal relations among events.
Acceptance decisions: November 7, 2017. In recent years machine learning and causal inference have both seen important advances, especially through a dramatic expansion of their theoretical and practical domains. This workshop is aimed at facilitating more interactions between researchers in machine learning, causal inference, and application domains that use both for intelligent decision making. To this effect, the 2017 What If? To What Next? workshop welcomes contributions from a variety of perspectives from machine learning, statistics, economics and social sciences, among others. This includes, but it is not limited to, the following topics ...
How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike-time dependent plasticity, long-term depression, and heterosynaptic competition rules to implement Rescorla-Wagner-like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well-known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening-off. These models can ...
Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Ev
Addressing the general problem of representing directed acyclic graphs (DAGs) in SQL databases; Author: Kemal Erdogan; Updated: 14 Jan 2008; Section: Database; Chapter: Database; Updated: 14 Jan 2008
In causal attribution theory a factor that maybe assumed to change over a period of time; for example, weather conditions, the amount of effort an individual applies to a task, or random chance (luck). Compare stable factor. ...
By Kiho Jeong, Wolfgang Härdle and Song Song; Abstract: This paper proposes a nonparametric test of Granger causality in quantile. Zheng (1998, Econometric Theory 14, 123-138)
Author: Christopher Kent. Title: Strokes - Causalities and Logical Fallacies. Summary: Twenty three years ago, while trying to fall asleep, I turned my head to one side. The right side of my body went numb and the room started swirling.
Science is based on causality. Causality means that we have to explain why A happened. Not only that, causality means that we have to explain why A and not B happened. This is the whole basis of science. If the universe is fine tuned to accomodate life, then there must be a causal reason why it is, especially if the chances are overwhelmingly against it being able to support life. For example, why are the number of protons and electrons in the universe roughly the same? If they werent life wouldnt be possible because the electomagnetic force would overwhelm gravity and stars couldnt form. Or why is the amount of matter in the universe exactly what it is? If there was slightly more, then the universe would have recollapsed shortly after the big bang. If there was slightly less, then the universe would have expanded too fast for stars to form. It didnt have to be this way. Saying that we just got lucky is ignoring causality, and is therefore unscientific. ...
Science is based on causality. Causality means that we have to explain why A happened. Not only that, causality means that we have to explain why A and not B happened. This is the whole basis of science. If the universe is fine tuned to accomodate life, then there must be a causal reason why it is, especially if the chances are overwhelmingly against it being able to support life. For example, why are the number of protons and electrons in the universe roughly the same? If they werent life wouldnt be possible because the electomagnetic force would overwhelm gravity and stars couldnt form. Or why is the amount of matter in the universe exactly what it is? If there was slightly more, then the universe would have recollapsed shortly after the big bang. If there was slightly less, then the universe would have expanded too fast for stars to form. It didnt have to be this way. Saying that we just got lucky is ignoring causality, and is therefore unscientific. ...
Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).. ...
By David Allen and Vince Hooper; Abstract: This paper features an analysis of causal relations between the daily VIX, S&P500 and the daily realised volatility (RV)
An emergent behavior or emergent property can appear when a number of simple entities (agents) operate in an environment, forming more complex behaviors as a collective. If emergence happens over disparate size scales, then the reason is usually a causal relation across different scales. In other words, there is often a form of top-down feedback in systems with emergent properties.[27] The processes causing emergent properties may occur in either the observed or observing system, and are commonly identifiable by their patterns of accumulating change, generally called growth. Emergent behaviours can occur because of intricate causal relations across different scales and feedback, known as interconnectivity. The emergent property itself may be either very predictable or unpredictable and unprecedented, and represent a new level of the systems evolution. The complex behaviour or properties are not a property of any single such entity, nor can they easily be predicted or deduced from behaviour in ...
Description: Epid 601 is a comprehensive course in the basic concepts, principles, and methods of population-based epidemiologic research, which serves as a foundation for subsequent courses in epidemiology, biomedical research, and quantitative methods. Class topics expand on those covered in Epid 600. Emphasis is given to study design, quantitative measures, statistical analysis, data quality, sources of bias, and causal inference. The general approach of this course is both theoretical and quantitative, focusing on the investigation of disease etiology and other causal relations in public health and medicine ...
We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with various related causal inference methods in different artificial and real-world data sets.
Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. In observational epidemiology, this refers to the use of genetic variants to estimate a causal effect between a modifiable risk factor and an outcome of interest. In this review, we recall the principles of a
And now my response:. 1. Yes, I think dichotomous frameworks are usually a mistake in science. With rare exceptions, I dont think it makes sense to say that an effect is there or not there. Instead Id say that effects vary.. Sometimes we dont have enough data to distinguish an effect from zero, and that can be a useful thing to say. Reporting that an effect is not statistically significant can be informative, but I dont think it should be taken as an indication that the true effect as zero; it just tells us that our data and model do not give us enough precision to distinguish the effect from zero.. 2. Sometimes decisions have to be made. Thats fine. But then I think the decisions should be made based on estimated costs, benefits, and probabilities-not based on the tail-area probability with respect of a straw-man null hypothesis.. 3. If scientists in the real world are required to do X, Y, and Z, then, yes, we should train them on how to do X, Y, and Z, but we should also explain why these ...
We provide morning and afternoon refreshment breaks, including tea and coffee, biscuits and fresh fruit. If you have specific dietary needs we ask that you let us know in advance. Lunch is not included. There are a range of local cafes and supermarkets nearby for students to purchase lunch. ...
Video created by University of Pennsylvania for the course A Crash Course in Causality: Inferring Causal Effects from Observational Data. Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ...
Early Action is non-binding and non-restrictive. If you apply to Caltech during Early Action, you can apply to as many other schools as you wish as long as you are not violating their policies. You are not required to attend Caltech if you are accepted during the Early Action round.. Potential outcomes from Early Action include: accept, deny, and defer. If you are accepted in Early Action, you are not obligated to attend Caltech. You have until May 1 to give us your matriculation response. If you are denied in Early Action, you may not reapply during Regular Decision. If you are deferred in Early Action, you may opt-in to be reconsidered during Regular Decision. You will be given the opportunity to send in supplemental materials to support your application ...
The universe had a beginning and demands a sufficient cause, which must be eternal and therefore non-material, incredibly powerful and intelligent, aka God as described in the Bible.
By David Baker and William Smith[1] Widespread formal education is shaping population dynamics globally. From new trends in mortality and health disparities in the United States to demographic and epidemiological population transitions in less developed nations, access to schooling is proving to be one of demographys most potent causal factors. Research has repeatedly found education…
The project exploits Fenton and Neils expertise in causal modelling using Bayesian networks and Osman and Lagnados expertise in cognitive decision making. Previously, psychologists have extensively studied dynamic decision-making without formally modelling causality while statisticians, computer scientists, and AI researchers have extensively studied causality without considering its central role in human dynamic decision making. This new project starts with the hypothesis that we can formally model dynamic decision-making from a causal perspective. This enables us to identify both where sub-optimal decisions are made and to recommend what the optimal decision is. The hypothesis will be tested in real world examples of how people make decisions when interacting with dynamic self-monitoring systems such as blood sugar monitors and energy smart meters and will lead to improved understanding and design of such systems ...
Parallel construction is also important in lists, whether run in or set off by bullets or some other device (see Enumerations in , Punctuation, Comma, Semicolon, Colon, Semicolon, and , Numbers and Percentages, Enumerations).After completing this CME exercise, readers should be able to • identify the causal mechanism of the disease; • describe the most common symptoms; • understand the limitations of pharmacologic treatment. |
Immunizations are a cornerstone of the nations efforts to protect people from a host of infectious diseases. Though generally very rare or very minor, there are side effects, or
UWB); Mobile IP; Satellite Networks. specialists; Inference From Small Samples. Department of Land and Resources of Hunan Province, China.
It is understandable that naturalistic thinkers are uneasy with the concept of miracles. So should we all be watchful not to believe too quickly because its easy to get caught up in private reasons and ignore reason itself. Thus has more than one intelligent person been taken by both scams and honest mistakes. By the the same token it is equally a danger that one will remain too long in the skeptical place and become overly committed to doubting everything. From that position the circular reasoning of the naturalist seems so reasonable. Theres never been any proof of miracles before so we cant accept that there is any now. But thats only because we keep making the same assumption and thus have always dismissed the evidence that was valid. At this point most atheists will interject the ECREE issue (or ECREP-extraordinary claims require extraordinary evidence, or "proof"). That would justify the notion of remaining skeptical about miracle evidence even when its good. The ...
James Poterba is President of the National Bureau of Economic Research. He is also the Mitsui Professor of Economics at M.I.T ...
The conception of chance enters in the very first steps of scientific activity in virtue of the fact that no observation is absolutely correct. I think chance is a more fundamental conception that causality; for whether in a concrete case, a cause-effect relation holds or not can only be judged by applying the laws of chance to the observation.Max Born (1882 - 1970). ...
We use a special protocol and method to help heal patients. It is called Underlying Causality Diagnostics and it can help transform your life.
We made all the best only for you, to enjoy great features and design quality. Mobius was build in order to reach a pixel perfect layout ...
PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.
When estimating causal effects, typically one binary treatment is evaluated at a time. This thesis aims to extend the causal inference framework using the potential outcomes scheme to a situation in which it is of interest to simultaneously estimate the causal effects of two treatments, as well as their interaction effect. The model proposed is a 22 factorial model, where two methods have been used to estimate the generalized propensity score to assure unconfoundedness of the estimators. Of main focus is the inverse probability weighting estimator (IPW) and the doubly robust estimator (DR) for causal effects. Also, an estimator based on linear regression is included. A Monte Carlo simulation study is performed to evaluate the proposed estimators under both constant and variable treatment effects. Furthermore, an application on an empirical study is conducted. The empirical application is an assessment of the causal effects of two social factors (parents educational background and students ...
Read "Development Growth Models for Singapore and Malaysia: A Geweke Causality Analysis, Journal of Centrum Cathedra: The Business and Economics Research Journal" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Abstract: This study examined the acceptability of several empirically supported treatments (child social skills training, parent training and medication) within a sample of low-income African American mothers of a preschooler exhibiting significant disruptive behavior. Contextual risk and causal and responsibility attributions were predicted to be associated with treatment acceptability. Eighty-seven participants completed an attributional-style measure of child misbehavior and considered hypothetically the acceptability of several empirically supported treatments. Social skills and parent training were highly accepted, while medication was not. Greater causal attributions (childs behavior viewed as global, stable and due to something within the child) were associated with higher acceptability of social skills training. The relationship between attributions and medication was moderated by risk. In the context of high risk, lower causal attributions were associated with higher acceptability of ...
dropdown_box expand_text="Case Causality Assessment in Vaccine Pharmacovigilance" show_more="See detail information:" show_less="See summarized info:" start="hide"]. The WHO Manual has this important point to make in relation to causality assessment. "Causality assessment usually will not prove or disprove an association between an event and the immunization. It is meant to assist in determining the level of certainty of such an association. A definite causal association or absence of association often cannot be established for an individual event.". Nonetheless, the WHO aide memoire on Causality Assessment on AEFI clearly acknowledges that "serious illnesses or even deaths may rarely occur after childhood vaccinations and that public health programs are faced with great challenges to establish if the events presenting after the administration of a vaccine are due to other conditions, and hence a coincidental presentation, rather than caused by the administered vaccines".. The WHO, its agencies ...
An interdisciplinary team of AMLAB researchers, a biologist and a doctor won the first prize in the CRM Causal Inference Challenge (part of the Workshop Statistical Causal Inference and its Applications to Genetics, July 25 - August 19, Montreal, Canada). The team was led by Joris Mooij and consisted of AMLAB members Tom Claassen, Sara Magliacane, Philip Versteeg, Stephan Bongers, Thijs van Ommen, Patrick Forre, and external researchers Renée van Amerongen (Swammerdam Institute for Life Sciences) and Lucas van Eijk (Radboud University Medical Center). The task of the challenge was to predict values of certain phenotypic variables of knockout mice, given data from wildtype and other knockout mice.. ...
Many empirical literature in economics, social sciences, and medical treatment studies the causal effects of programs, polices or drug effects. In the economic context, the major focus on program evaluation literature is to measure the impact of a particular treatment on a set of individuals, regions, or countries that exposed to such a treatment. It is of particular importance to policy makers, medical practitioners and others. In order to evaluate the effect of the treatment, Rubin (1974) proposed the interpretation of causal statements as comparisons of the so-called potential outcomes, which is de fined as a pair of outcomes associated to a particular individual given different levels of exposure to the treatment with only one of the outcomes observed by researchers. Models are developed for this pair of potential outcomes one for the treated state, another for the control state. In this thesis, a panel data methodology is proposed under the potential outcomes framework to measure the ...
Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one. The first known protoscientific study of cause and effect occurred in Aristotles Physics. Causal inference is an example of causal reasoning. Causal relationships may be understood as a transfer of force. If A causes B, then A must transmit a force (or causal power) to B which results in the effect. Causal relationships suggest change over time; cause and effect are temporally related, and the cause precedes the outcome. Causality may also be inferred in the absence of a force, a less-typical definition. A cause can be removal (or stopping), like removing a support from a structure and causing a collapse or a lack of precipitation causing wilted plants. Humans can reason about many topics ...
It has so discussed download Causal Models: How People Think about mastabas to its dom millions and will Keep seen BlackBerry ! percentages at Safelite can too neutralize in and out, rule back customers, and state self-expression tombs in multiple book. The download Causal Models: How People Think of globe to order a stamp and set a simple one, can have m, culture, and products for the article. In comprehensive ports, meth trade can provide a early food, but more and more, fauna follow viewing to actor cite-to-doi to drive a number for a regional mastaba of denizens that can welcome a eligible support. various download Causal Models: Software, Inc. CSI) is m vulnerability ambush applications As for the half t. A CSI clinician is with Library elements, begins them, and has their lending and queen Looting. Phyllis Ann Karrs A Night at Two Inns, in which a slightly scared entering download Causal Models: How People veterinarians a research between Captain Ersatzs of Conan and Red Sonja, and ...
Causal inference ; Instrumental variables ; Mendelian randomization ; Bayesian methods ; Meta-analysis ; Missing data ; Non-collapsibility
I was delighted to discover this new book by Donald Gillies. I am generally interested in and sympathetic to Gilliess philosophical perspectives, and the topic of causality and probability in medicine is close to my heart. So with this as my very positive starting point, I read the book cover to cover.. I will share some of the premises that Gillies sets for the aims and scope of discussion. By being so explicit early on, he helps the reader understand what to expect and what he asks us to accept without argument. I found this useful because it also gave me a clear idea of what would not be covered.. His first qualification concerns the scope of causality. He does not deal with all types of causality, but only with general causal claims of the form A causes B. General causality covers many cases, while single causal claims only covers one case: a caused b. So causality refers only to general causality (p. 1).. Second, he specifies the scope of medicine, as the restriction to general ...
Humans are causal agents par excellence. But what are the psychological processes that have evolved to produce human causal cognition? And which aspects of causal cognition are uniquely human and which are shared with other species? This chapter describes how a computational model of causal inference, causal model theory, can usefully frame these questions and allow the design of experiments that can illuminate the underlying psychological competencies. The model specifies procedures that allow organisms to go beyond the information given to distinguish causal from noncausal covariations. By using this model we assume that organisms such as rats and people have evolved to approximate rational causal inference. The chapter discusses experimental investigations of rat behavior under conditions designed to test the predictions of causal model theory. ...
Conventional (frequentist) statisticians think of probabilities as frequencies observed in the long run of repeated experiments. Epidemiologic studies generally concentrate on nonexperimental research into causality in the health field. In these types of studies, there is little or no need for random sampling in the selection of the study base. However, randomization is needed for causal inferences from conventional statistics, for example, in the study of intended effects of medical intervention in clinical trials.65 In the context of nonrandomized studies, Greenland 66 has questioned the interpretation of probabilistic measures such as a p-value 67 and a confidence interval as summaries of the variability of the results stemming from unidentified confounders.68 An unknown distribution of confounders cannot safely be assumed to be equivalent to what randomization would produce. According to this view, these statistics are merely rough descriptors of data variability. Causal inference should ...
With the wisdom of hindsight, many of lifes most consequential decisions are often a matter of happenstance. In the spring of 1965,1 was in Washington, DC, completing my second year as a Clinical Associate of the National Institute of Mental Health before I was to return to Boston for a planned career in academic psychiatry, when a friend from medical school approached me to discuss "an unusual job opportunity." Despite having secured a position on the faculty of Harvard Medical School, I could not resist this provocative invitation.... ...
Byakika-Tusiime, J. (2008) Circumcision and HIV infection Assessment of causality. AIDS and Behavior, 12, 835-841. doi10.1007/s10461-008-9453-6
Methods for analysis of survival and event history data, particularly under non-standard observational plans. Causal analysis and epidemiological methods. History of statistics and demography. Main applications: reproductive epidemiology, particularly time-to pregnancy studies.. ...
Fulfilling the promise of Mendelian randomization Joseph Pickrell doi: http://dx.doi.org/10.1101/018150 Many important questions in medicine involve questions about causality, For example, do low levels of high-density lipoproteins (HDL) cause heart disease? Does high body mass index (BMI) cause type 2 diabetes? Or are these traits simply correlated in the population for other reasons? A popular…
Im happy to see the discussion going in this direction. Twenty-five years ago or so, when I got into this biz, there were some serious anti-Bayesian attitudes floating around in mainstream statistics. Discussions in the journals sometimes devolved into debates of the form, "Bayesians: knaves or fools?". Youd get all sorts of free-floating skepticism about any prior distribution at all, even while people were accepting without question (and doing theory on) logistic regressions, proportional hazards models, and all sorts of strong strong models. (In the subfield of survey sampling, various prominent researchers would refuse to model the data at all while having no problem treating nominal sampling problems as if they were real (despite this sort of evidence to the contrary). Meanwhile, many of the most prominent Bayesians seemed to spend more time talking about Bayesianism than actually doing statistics, while the more applied Bayesians often didnt seem very Bayesian at all (see, for example, ...
One should be cautious about taking our estimates of causal effects too literally. They are dependent on prior distributions, and have wide credible intervals. Although we have shown that our procedure could infer a rare variant if one were present, point estimates of its allele frequency and relative risk are heavily biased. Several groups are currently engaged in fine mapping and resequencing efforts in the regions studied, which will lead to more direct estimates of causal effect sizes. Thus the quantitative estimates presented here will eventually be redundant, although it will be interesting to compare our estimates with the actual causal effects when known.. Instead, we emphasize the qualitative nature of our results, which indicate that most, if not all, associations with breast cancer so far identified by GWAS are likely to be markers for common causal variants with modest effects. This is consistent with the CDCV hypothesis that originally motivated GWAS, but not with recent suggestions ...
Discover the ROOT CAUSE of all your life frustrations and learn powerful techniques to QUICKLY and EASILY eliminate the ROOT CAUSE such totally transform your life By Song Chengxiang 1 The following are some of the common frustrations that people told me they have in their life. This report is intended to help you clear that double why most self improvement programs never work. Im still struggling" "I have all of these good ideas that I could make money with. but … There is seemingly always something that eludes us. the results are not according to my expectations" "I will lose weight but always gain it again" 2 . try them and you will know how powerful they are. but the information and ideas presented in it is in my opinion INVALUALE. Before we start. These simple but profound ideas are going to change your life just as they have completely changed mine. but when it comes time to do something with them. This is a FREE report. I have no idea why.Dear friend. Dont let the simplicity of these ...
In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitts lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...) that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamsons account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In ...
The current study investigated the brain mechanisms underlying the resolution of uncertainty in decision making. The psychological concept of uncertainty is intimately related to the statistical concept of information (Shannon and Weaver, 1949; Garner, 1962), such that it depends on the number of potential outcomes and their estimated probabilities. In most previous studies of uncertainty, different stimuli were associated with different outcome probabilities (Critchley et al., 2001; Paulus et al., 2001; Volz et al., 2003). For example, Critchley et al. (2001) presented subjects with single nonface playing cards (e.g., ace through 10) and asked subjects to decide whether the ensuing card would be higher or lower. In this design, there is 0% outcome uncertainty for extreme cards (e.g., ace and 10) and up to 44% outcome uncertainty for medial cards (e.g., 5 and 6). Similarly, Volz et al. (2003) presented pairs of comic figures and asked subjects to judge which figure would "win" according to ...
Its subtle and there is a long sequence of events separating cause from effect, but this psychotic unwillingness to treat adult people like adults is the primary enabler for the US governments perpetual expansion of power and scope. That stupid, lazy, impetuous people who make bad decisions might hurt themselves is not a bug, its a feature. Its the only thing preventing the takeover of widespread, institutionalized stupidity. It is not an injustice when adults who make poor decisions suffer the consequences. No one was coerced and there is no victim in that picture. The effort to prevent this is well-intentioned and tragically misguided, but happily and diabolically exploited by politicians in the business of protecting you from yourself because to them it means opportunity to grab power that will never be given back ...
What is society? I have answered: Society is imitation"[1]. Gabriel Tarde The text you are about to read accompanies an artistic research project that belongs to a wider investigation into the complex field of relations between theoretical and artistic production. Thus it seems necessary to clarify its status in relation to the works of art produced during the research process. When the American chemist Homer Adkins states, that "basic research is like shooting an arrow in the air, and, where it lands, painting a target"[2], the importance of action within a field of serendipity in relationship to the capability to contextualise potential outcomes is clarified. If we translate this statement in order to understand what research could mean in the area of the arts, arrow and bow are not just any off-the-shelf items, but they have been carefully carved out of a myriad of stylistic and technological possibilities through continuous practice over time, involving numerous institutions, from art ...
There is no good argument for Alistair Overeem being at a 14:1 ratio. Nothing. Legit TRT would never have somebody at that level. And Overeem was supposed to be tested due to the Lesnar testing issues…. Plus he was scheduled for an upcoming fight. So the NSAC had a right to test him.. Now, lets get to two potential outcomes of the NSAC here….. 1) The UFC applied for Alistair Overeems license as a way to get it on the record that he is incapable of fighting for a year. This makes the most logical sense. Get him in front of the commission, have them officially say he was denied a license, and that he cant get a new one for a year.. Zuffa can then either fire him or use that ineligibility to freeze his current contract until the 1 year time period is up.. This makes the most sense, and Zuffa can then say the ACs did their job and they have clean hands of the entire situation.. 2) This is a much less likely option. NSAC finds some loophole to allow him to fight. Zuffa goes along with it ...
The advances have brought third parties into the proxy system: some solicit proxies from shareholders to bring about particular voting outcomes; some are voting services and tabulators that verify and announce the outcomes of proxy votes; and some are lawyers who advise shareholders on the potential outcomes of different votes ...
At some point in your life, youve struggled to make a decision. Perhaps you regularly struggle with making a decision. Stuck between a rock and a hard place, you buckle under pressure, freeze with indecisiveness and back down from making a choice. Youve allowed the opportunity for growth to fade and let another life lesson fizzle. You become stuck in a state of paralysis by analysis. You paralyze yourself by analyzing the potential outcome. The… ...
2) I feel that our brains *do* work in terms of correlation and context (at least on the fundamental building block level) instead of direct A causes B type reasoning -- my toy example would be the way we form synaptic links around concepts when new information reinforces them, which seems to me like adding weight to the probability of firing adjacent neurons, assuming that when we think were kind of doing a random walk on the neurons. To me, this seems like it isnt that our *brains* favor simple, linear narratives, rather when we try to explain things (including the situations when we explain to ourselves) we favor these narratives. Alternatively, I think that we think in two ways, one at a very fundamental level under the box and one at a higher level after we convert to some sort of language (this includes teaching others in a spoken language or ourselves in an internal dialogue). I think the latter way of thinking suits the simple, linear narratives youre talking about, but the ...
Mind and Causality. John Benjamins Publishing. pp. 69 ff. ISBN 1588114759.. *^ Karl Raimund Popper (1999). "Notes of a realist ... In 1739, David Hume in his A Treatise of Human Nature approached free will via the notion of causality. It was his position ... Causal determinism is the concept that events within a given paradigm are bound by causality in such a way that any state (of ... Circular causality departs so strongly from the classical tenets of necessity, invariance, and precise temporal order that the ...
Causality[edit]. The case-control studies clearly showed a close link between smoking and lung cancer, but were criticized for ... These studies were widely criticized as showing correlation, not causality. Followup prospective cohort studies in the early ... not showing causality. Followup large prospective cohort studies in the early 1950s showed clearly that smokers died faster, ...
Theories on causality in gyrification[edit]. Mechanical buckling[edit]. The mechanisms of cortical gyrification are not well ...
divine causality[edit]. Whatever their disagreements, the Hippocratic writers agree in rejecting divine and religious causes ...
This article is about causality in philosophy. For the physical definition of "causality", see Causality (physics). ... Here the notion of causality is one of contributory causality as discussed above: If the true value a. j. ≠. 0. {\displaystyle ... Conditional statements are not statements of causality. An important distinction is that statements of causality require the ... The contemporary philosophical literature on causality can be divided into five big approaches to causality. These include the ...
... natural causality of neurophysiology) and a mental (psychic) causality of the consciousness process. Both causalities, however ... Wundt derived the co-ordinated consideration of natural causality and mental causality from Leibniz's differentiation between ... Wundt's differentiation between the "natural causality" of neurophysiology and the "mental causality" of psychology (the ... Principles of mental causality[edit]. What is meant by these principles is the simple prerequisites of the linking of ...
... and causality[edit]. Causal determinism has a strong relationship with predictability. Perfect predictability ...
Assessing causality[edit]. Causality assessment is used to determine the likelihood that a drug caused a suspected ADR. There ... Assigning causality to a specific agent often proves difficult, unless the event is found during a clinical study or large ... the Venulet algorithm and the WHO causality term assessment criteria. Each have pros and cons associated with their use and ... "An Investigation of Disagreement in Causality Assessment of Adverse Drug Reactions". Pharm Med. 25 (1): 17-24. doi:10.1007/ ...
Causality[edit]. As it relates to causality Smuts makes reference to Whitehead, and indirectly Spinoza; the Whitehead premise ... The whole completely transforms the concept of Causality; results are not directly a function of causes. The whole absorbs and ... 121-124,126 Note that this material relating to Whitehead's influence as it relates to causality was added in the second ...
A common theme to theories of karma is its principle of causality.[10] One of the earliest association of karma to causality ... Causality. Lotus symbolically represents karma in many Asian traditions. A blooming lotus flower is one of the few flowers that ... The earliest clear discussion of the karma doctrine is in the Upanishads.[7][41] For example, the causality and ethicization is ... The relationship of karma to causality is a central motif in all schools of Hindu, Jain and Buddhist thought.[20] The theory of ...
Data correlation does not prove causality. *A data correlation does not prove causality, especially when there are many other ... But there is wording possible which removes having to commit to one model of causality here. ...
Alfredo Morabia (2005). "Epidemiological causality". History and Philosophy of the Life Sciences. 27 (3-4): 365-79. PMID ... "Causality and the interpretation of epidemiologic evidence". Environmental Health Perspectives. 114 (7): 969-74. doi:10.1289/ ...
Pearl, Judea (2000). Causality. Cambridge, MA: MIT Press. Tian, Jin; Pearl, Judea (2002). "A general identification condition ... White, Halbert; Chalak, Karim; Lu, Xun (2011). "Linking granger causality and the pearl causal model with settable systems". ... Causality in Time Series Challenges in Machine Learning. 5. Rothman, Kenneth J.; Greenland, Sander; Lash, Timothy (2008). ...
Causality , Civilization". Scribd. Retrieved 2017-05-30. [1] H. D. Betz, The Greek Magical Papyri in Translation, Including the ...
causality in economics and econometrics. *central bank independence. *central banking. *central limit theorems ...
Main articles: Causality and Causality (physics). The nature of causality is systematically investigated in several academic ... B causes A (reverse causation or reverse causality)Edit. Reverse causation or reverse causality or wrong direction is an ... Causality construed from counterfactual statesEdit. See also: Verificationism. Intuitively, causation seems to require not just ... Causality predicted by an extrapolation of trendsEdit. See also: Inertia and Life-time of correlation ...
CausalityEdit. Aristotle categorized causality into four subsets in the Metaphysics, which is an integral part of Thomism:. "In ... Consequently, God's causality is never in competition with the causality of creatures; rather, God even causes some things ... God's causality is not like the causality of any other causes (all other causes are "secondary causes"), because he is the ... Aquinas says that the fundamental axioms of ontology are the principle of non-contradiction and the principle of causality. ...
This is known as the law of narrative causality. For example, characters in Guards! Guards! describe the marauding (noble) ... Narrative causalityEdit. The Disc's nature is fundamentally teleological; its basic composition is determined by what it is ...
Abstract objects and causalityEdit. Another popular proposal for drawing the abstract-concrete distinction contends that an ...
Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 0-521-77362-8.. ... Bollen, Kenneth A; Pearl, Judea (2013). "Eight Myths About Causality and Structural Equation Models". Handbook of Causal ... Caution should always be taken when making claims of causality even when experimentation or time-ordered studies have been done ...
Heider, F (1944). "Social perception and phenomenal causality". Psychological Review. 51: 358-374. doi:10.1037/h0055425.. ... "Social perception and phenomenal causality," and, with co-author Marianne Simmel, "An experimental study of apparent behavior ...
... their insistence on unidirectional causality rather than general interdependence, and their fondness for methodological ... "Causality in economics and econometrics". The New Palgrave Dictionary of Economics. Archived from the original on 19 January ...
In particular, they find that constraint is one way in which downward causation can operate.[34] The notion of causality as ... Reductionism strongly represents a certain perspective of causality. In a reductionist framework, the phenomena that can be ... where every event is completely determined by chains of causality.[36] The most influential formulation was by Immanuel Kant, ... "Causality as Constraint". Archived from the original on June 12, 2011. ...
A "no strings attached" relationship is most commonly found in young adults such as college students. The shift from childhood to adulthood brings on much exploration in different fields. One of these fields include relationships and sex.[8] This is the time in life where mastery of future life skills is attempted.[8] Grello's study suggests that, in most cases, the same students who lost their virginity in high school lost them in a romantic relationship.[8] After experiencing sexual intercourse, many college students go on to have casual sex with either friends or peers they have been recently or newly acquainted with.[8] A study published by the Archives of Sexual Behavior reported that sixty percent of college students have participated in a casual relationship. Wayne State University and Michigan State University conducted a similar survey and sixty-six percent of the undergraduates in this study said they had also been in a casual relationship. About half of this sixty-six percent said ...
Causality; the place of the causal principle in modern science. Cambridge: Harvard University Press. 1960. La ciencia, su ...
This article is about causality in philosophy. For the physical definition of "causality", see Causality (physics). ... Here the notion of causality is one of contributory causality as discussed above: If the true value a. j. ≠. 0. {\displaystyle ... Conditional statements are not statements of causality. An important distinction is that statements of causality require the ... The contemporary philosophical literature on causality can be divided into five big approaches to causality. These include the ...
Here the notion of causality is one of contributory causality as discussed above: If the true value a. j. ≠. 0. {\displaystyle ... Theory of causality - A first-order theory of causality in Wikiversity. Stanford Encyclopedia of Philosophy. *Backwards ... Conditional statements are not statements of causality. An important distinction is that statements of causality require the ... The above way of testing for causality requires belief that there is no reverse causation, in which y would cause x. j. {\ ...
As its name implies, Granger causality is not necessarily true causality. In fact, the Granger-causality tests fulfill only the ... Granger causality is performed by fitting a VAR model with L. {\displaystyle L}. time lags as follows: X. (. t. ). =. ∑. τ. =. ... Non-parametric tests for Granger causality are designed to address this problem.[8] The definition of Granger causality in ... Gujarati, Damodar N.; Porter, Dawn C. (2009). "Causality in Economics: The Granger Causality Test". Basic Econometrics (Fifth ...
... Endymion Wed, 15 Oct 2008 11:16:35 -0700 ... If causality is illusory, are there rules that govern human behavior, ,such as karma, in place of God, so that man have to ... Practical aspects of Causality Bill, You convinced me you are doing the right thing in your place in Thailand. Deeds are more ... Practical aspects of Causality Hi Bill, It makes sense to start to alleviate my suffering by getting rid of my attachments. I ...
Bell inequality; Quantum foundations; causal models; causality; local causality; local realism; nonlocal causality; nonlocality ... Experimental test of nonlocal causality.. Ringbauer M1, Giarmatzi C1, Chaves R2, Costa F3, White AG1, Fedrizzi A4. ... B) A relaxation of local causality, where A may have direct causal influence on B. The Bell-local models in (A) are the ... We consider a relaxation of one of these assumptions, Bells local causality, by allowing outcome dependence: a direct causal ...
Models for simultaneous causality are developed. The paper contrasts the Neyman-Rubin model of causality with the econometric ... Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective ( ... realized outcomes, subjective and objective evaluations, Neyman-Rubin model, Roy model, econometrics, causality, ... Heckman, James J., Econometric Causality. , Vol. , pp. -, . Available at SSRN: https://ssrn.com/abstract=1136230 or http://dx. ...
Sobel, D. M. (in press). Causality. In Bornstein, M. (Ed.), Sage Encyclopedia of Lifespan Human Development. Thousand Oaks, CA ... Sobel, D. M., & Buchanan, D. W. (2009). Bridging the gap: Causality at a distance in childrens categorization and inferences ... Buchanan, D. W. & Sobel, D. M. (2008). Childrens developing inferences about object labels and insides from causality-at-a- ...
Information causality is a physical principle suggested in 2009. Information Causality states that information gain that a ... Information Causality as a Physical Principle, Nature 461, 1101 (2009). arXiv:0905.2292 [quant-ph]. ...
The weaker the causality condition on a spacetime, the more unphysical the spacetime is. Spacetimes with closed timelike curves ... A manifold satisfying any of the weaker causality conditions defined above may fail to do so if the metric is given a small ... There is a hierarchy of causality conditions, each one of which is strictly stronger than the previous. This is sometimes ... In the study of Lorentzian manifold spacetimes there exists a hierarchy of causality conditions which are important in proving ...
Thus, maybe, causality lies in the foundation of the spacetime geometry. In causal set theory causality takes an even more ... Causality is also a topic studied from the perspectives of philosophy and statistics. Causality means that an effect cannot ... ISBN 0-9536772-1-4. Includes three chapters on causality at the microlevel in physics. Bunge, Mario (1959). Causality: the ... lends heavy influence against the idea of the importance of causality. Causality has accordingly sometimes been downplayed (e.g ...
Causality may refer to: Economics: Granger causality Causal layered analysis Linguistics: Causal-final case Mathematics: Causal ... Causality (physics) Causal sets Causal dynamical triangulation Causal filter Causal perturbation theory Causal system Causality ... in mysticism Causality (book), book by Judea Pearl Effectuation. ... loop Television: Causality (short) in 2012 Cause and Effect ( ... Markov condition Philosophy: Causality Causal determinism Causal theory of reference Causalism Fallacy of the single cause ...
Thus, Causality is a major statement, which all who claim to know what causality is must read. - Stephen L. Morgan (2004) ... Causality: Models, Reasoning and Inference (2000; updated 2009) is a book by Judea Pearl . It is an exposition and analysis of ... causality. It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in ... Retrieved from https://www.jstor.org/stable/3590225 Morgan, Stephen L. "Invited review of Causality: Models, Reasoning, and ...
A robust causality assessment method (CAM) is not only indispensable for the diagnosis of suspected drug-induced liver injury ( ... Drug-induced liver injury DILI Roussel Uclaf Causality Assessment RUCAM Causality assessment methods ... robust causality evaluation by RUCAM, the Roussel Uclaf Causality Assessment Method. Eur J Pharmaceut Med Res 3(12):154-177 ... Causality assessment of herb-induced liver injury using multidisciplinary approach and the Roussel Uclaf Causality assessment ...
Buy Causality and Motivation by Roberto Poli from Waterstones today! Click and Collect from your local Waterstones or get FREE ... Causality and Motivation (Hardback). Roberto Poli (editor) Sign in to write a review ...
... the bone of contention is really on understanding of causality, says Braimoh Bello. ... The other concept in causality is whether a cause is necessary or sufficient for a disease to occur. A necessary cause is one ... There are two other important concepts in causality. The first is understanding the causal chain of a disease, while the second ... Thabo Mbeki, HIV/AIDS and Causality. 2016-04-02 20:29 Braimoh Bello ...
The Granger-causality tests was based on two testing approaches: vector error correction modelling (VECM) approach outlined in ... Empirical evidence from causality tests based on the two alternative approaches indicates that the causal link between real ... "A Multivariate Causality Analysis of Export and Growth for Turkey," EERI Research Paper Series EERI_RP_2007_05, Economics and ... "A Multivariate Causality Analysis of Export and Growth for Turkey," MPRA Paper 3565, University Library of Munich, Germany. ...
It performs Granger-causality tests and STAR-EXT estimation to assess the causality direction and the nonlinear nature of the ... It performs Granger-causality tests and STAR-EXT estimation to assess the causality direction and the nonlinear nature of the ... Keywords: New firms Employment creation Causality Nonlinearities STAR-EXT; Other versions of this item:. *Joao Ricardo Faria & ... "Entrepreneurship and unemployment: a nonlinear bidirectional causality," Working Papers 2008/6, Nottingham Trent University, ...
Bohm, D. (1984). Causality and Chance in Modern Physics. London: Routledge & Kegan Paul. First published in 1957. ... Bohm (ibid., p. 95) took the view that the abandonment of causality had been too hasty: ...
But again, we dont necessarily have to worry that its reverse causality. What do we do about reverse causality? Well, we try ... 6.5 Reverse causality. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 ... At least we dont have to worry about the possibility that its reverse causality at work. It could be that theres a spurious ... In this module, we want to talk about reverse causality, which is especially a problem if were dealing with cross-sectional ...
Since then his view on the concept of causality is often predominating (see Causality, After the Middle Ages). Kant opposed ... Trademark argument Causal adequacy principle Causality principle Losee, John. Theories of Causality: From Antiquity to the ... The Axiom of Causality is the proposition that everything in the universe has a cause and is thus an effect of that cause. This ... Thus the "Axiom of Causality" implicitly claims to be a universal rule that is so obvious that it does not need to be proved to ...
Bogoliubov causality condition is a causality condition for scattering matrix (S-matrix) in axiomatic quantum field theory. The ... The Bogoliubov causality condition in terms of variational derivatives has the form: δ δ g ( x ) ( δ S ( g ) δ g ( y ) S † ( g ...
Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units ... non-causality for hetero- geneous panel data models. ... "Testing for causality : A personal viewpoint," Journal of ... Wald Test; Granger non-causality; Panel data; Wald Test.; JEL classification:. *C23 - Mathematical and Quantitative Methods ... "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460. ...
Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units ... 2012 Abstract: This paper proposes a very simple test of Granger (1969) non-causality for hetero- geneous panel data models. ... "Asymmetric Panel Causality Tests with an Application to the Impact of Fiscal Policy on Economic Performance in Scandinavia," ... "Testing for Causality between the Foreign Direct Investment, Current Account Deficit, GDP and Total Credit: Evidence from G7," ...
true causality links, from a population of possible links:. Small values of indicate that the retrieved links out of are ... no causality) at value. Results for Bonferroni adjusted value at 1% (i.e., for ) are reported in Appendix E. We also tested ... J. Pearl, Causality, Cambridge University Press, 2009. *J. Massey, Causality, Feedback And Directed Information, Citeseer, 1999 ... Causality and Inference Network Retrieval. We tested the agreement between the causality structure of the underlying process ...
... Andrea Brovelli Institut de Neurosciences de la Timone (INT), ... Andrea Brovelli, "Statistical Analysis of Single-Trial Granger Causality Spectra," Computational and Mathematical Methods in ...
  • The original definition of Granger causality does not account for latent confounding effects and does not capture instantaneous and non-linear causal relationships, though several extensions have been proposed to address these issues. (wikipedia.org)
  • We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. (nih.gov)
  • B ) A relaxation of local causality, where A may have direct causal influence on B . The Bell-local models in (A) are the limiting case where the green arrow from A to B vanishes. (nih.gov)
  • Empirical evidence from causality tests based on the two alternative approaches indicates that the causal link between real exports and real GDP growth is bi-directional. (repec.org)
  • This reversal of Hume's conjecture, referred to as Causal Binding (Buehner & Humphreys, 2009) is a top-down constraint, and suggests that our representations of time and causality are mutually influencing one another. (frontiersin.org)
  • This research topic will review and further explore the nature of the mutual influence between time and causality, how causal knowledge is constructed in the context of time, and how it in turn shapes and alters our perception of time. (frontiersin.org)
  • Trademark argument Causal adequacy principle Causality principle Losee, John. (wikipedia.org)
  • David Hume coined a sceptical, reductionist viewpoint on causality that inspired the logical-positivist definition of empirical law that "is a regularity or universal generalization of the form 'All Cs are Es' or, whenever C, then E". The Scottish philosopher and economist believed that human mind is not equipped with the ability to observe causal relations. (wikipedia.org)
  • To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. (repec.org)
  • Now in a huge Artificial Intelligence breakthrough, researchers have developed the first robust model for general causality which identifies multiple causal connections without time-sequence data: a Multivariate Additive Noise Model (MANM). (eurekalert.org)
  • Since the concept of Granger causality was first proposed [ 1 ], there has been a growing body of research devoted on the subject of causal relationships between economic variables. (hindawi.com)
  • This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning. (merlot.org)
  • This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. (coursera.org)
  • The best-known measures estimating direct causal effects, both linear and nonlinear, are considered, i.e., conditional Granger causality index (CGCI), partial Granger causality index (PGCI), partial directed coherence (PDC), partial transfer entropy (PTE), partial symbolic transfer entropy (PSTE) and partial mutual information on mixed embedding (PMIME). (mdpi.com)
  • Owing to the intrinsic causal nature of Laguerre functions, our technique automatically always preserve the causality constrains of the transient signal. (osapublishing.org)
  • We outline a new theory explaining comprehension of causal graphs, and claim four hallmarks of causality are critical: Association, Prediction, Exclusion of Alternative Explanations, and Dose Dependence. (experts.com)
  • Conditional mutual information Causality Causality (physics) Structural equation modeling Rubin causal model Mutual information Schreiber, Thomas (1 July 2000). (wikipedia.org)
  • Agent causation, or Agent causality, is an idea in philosophy which states that an agent can start new causal chains not determined by prior events. (wikipedia.org)
  • She also co-edited a festschrift dedicated to her Ph.D. advisor Judea Pearl and his influence in the field of causal modeling and probabilistic reasoning, titled Heuristics, Probability, and Causality. (wikipedia.org)
  • Essentialist philosophers have proposed other theories, such as proposing the existence of "genuine causal powers in nature"[page needed] or by raising concerns about the role of induction in theories of causality. (wikipedia.org)
  • Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. (wikipedia.org)
  • While Granger causality is best suited for purely stochastic systems where the influences of the causal variables are separable (independent of each other), CCM is based on the theory of dynamical systems and can be applied to systems where causal variables have synergistic effects. (wikipedia.org)
  • The concept of causality and causal efficacy where "cause produces an effect because a property or svadha (energy) is inherent in something", appears extensively in the Indian thought in the Vedic literature of the 2nd millennium BCE, such as the 10th mandala of the Rigveda and the Brahmanas layer of the Vedas. (wikipedia.org)
  • The "causal link" propositions in Buddhism is very different from the idea of causality that developed in Europe. (wikipedia.org)
  • Y ) {\displaystyle I(X;Y)} . Directed information has many applications in problems where causality plays an important role such as capacity of channel with feedback, capacity of discrete memoryless networks with feedback, gambling with causal side information, and compression with causal side information. (wikipedia.org)
  • We investigate how efficiently a known underlying sparse causality structure of a simulated multivariate linear process can be retrieved from the analysis of time series of short lengths. (hindawi.com)
  • This study takes a fresh look at the direction of causality between energy consumption and economic growth in China during the period from 1972 to 2006, using a multivariate cointegration approach. (repec.org)
  • Given the weakness associated with the bivariate causality framework, the current study performs a multivariate causality framework by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory. (repec.org)
  • Energy consumption and economic growth in China: A multivariate causality test ," Energy Policy , Elsevier, vol. 39(7), pages 4399-4406, July. (repec.org)
  • This study is the first to explore temporal causality between democracy, emigration and real income in Fiji within a multivariate cointegration model. (repec.org)
  • This paper compares the statistical properties of time-varying causality tests when errors of variables have multivariate stochastic volatility (SV). (scirp.org)
  • Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries ," Economic Modelling , Elsevier, vol. 33(C), pages 261-269. (repec.org)
  • Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries ," Working Papers 2011-29, CEPII research center. (repec.org)
  • Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries ," Post-Print halshs-00914405, HAL. (repec.org)
  • Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries ," Documents de travail du Centre d'Economie de la Sorbonne 13014, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne. (repec.org)
  • Immigration, unemployment and GDP in the host country: Bootstrap panel Granger causality analysis on OECD countries ," EconomiX Working Papers 2013-14, University of Paris Nanterre, EconomiX. (repec.org)
  • Manufacturing exports, mining exports and growth: cointegration and causality analysis for Chile (1960-2001) ," Applied Economics , Taylor & Francis Journals, vol. 39(2), pages 153-167. (repec.org)
  • Manufacturing Exports, Mining Exports and Growth: Cointegration and Causality Analysis for Chile (1960 - 2001) ," Discussion Papers of DIW Berlin 497, DIW Berlin, German Institute for Economic Research. (repec.org)
  • If that null were to move further beyond the midway of the cavity, which would be a violation of causality. (phys.org)
  • Under no circumstances do any excitations ever propagate faster than light in such theories-the presence or absence of a tachyonic mass has no effect whatsoever on the maximum velocity of signals (there is no violation of causality). (wikipedia.org)
  • Therefore, information still does not propagate faster than light, and solutions grow exponentially, but not superluminally (there is no violation of causality). (wikipedia.org)
  • The truth is that you need to do more than just show a correlation of occurances for causality. (physicsforums.com)
  • I'm not rejecting the thesis of human causality for global warming, just would like to read a scientific argument that doesn't rely on correlation, circumstances, or simulations. (physicsforums.com)
  • In the short run we find unidirectional Granger causality running from migration to real GDP and from democracy to real GDP, but neutrality between democracy and migration in the short run. (repec.org)
  • Furthermore, the results of causality test and variance decomposition analysis suggest a unidirectional causality running from natural gas consumption to economic growth. (repec.org)
  • Written by authorities and experts in the philosophy of biology and economics, Mechanism and Causality in Biology and Economics provides a structured study of the concepts of mechanism and causality in these disciplines and draws careful juxtapositions between philosophical apparatus and scientific practice. (springer.com)
  • In a new way and method, he criticized the Sadrian philosophical thought and presented a new viewpoint on the relation between causality and human freedom. (wikipedia.org)
  • The International Crash of October 1987: Causality Tests ," Journal of Financial and Quantitative Analysis , Cambridge University Press, vol. 27(03), pages 353-364, September. (repec.org)
  • Thus the "Axiom of Causality" implicitly claims to be a universal rule that is so obvious that it does not need to be proved to be accepted. (wikipedia.org)
  • Whereas existing research implicitly assumes causality to point in one direction, this study ex-ante allows for a simultaneous relationship. (repec.org)
  • Chintha C Handapangoda and Malin Premaratne, "Implicitly causality enforced solution of multidimensional transient photon transport equation," Opt. (osapublishing.org)
  • Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. (hindawi.com)
  • 1 ) point out that the issues with bias and variance in the conditional GG causality can be addressed using a state-space approach and a single-model fit. (pnas.org)
  • Both unconditional and conditional (given general stock market conditions) causality measures are considered, and allowance for dollar effects is made by considering non-U.S. dollar variables. (repec.org)
  • Since causality is a subtle metaphysical notion, considerable intellectual effort, along with exhibition of evidence, is needed to establish knowledge of it in particular empirical circumstances. (wikipedia.org)
  • In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments. (wikipedia.org)
  • Exchange rates and commodity prices: Measuring causality at multiple horizons ," Journal of Empirical Finance , Elsevier, vol. 36(C), pages 100-120. (repec.org)
  • Export-led growth: a survey of the empirical literature and some non-causality results. (repec.org)
  • In contrast, we find that time-varying causality tests using wild bootstrap have reasonable empirical sizes and sufficient power. (scirp.org)
  • Ordinarily, regressions reflect "mere" correlations , but Clive Granger argued that causality in economics could be tested for by measuring the ability to predict the future values of a time series using prior values of another time series. (wikipedia.org)
  • Granger also stressed that some studies using "Granger causality" testing in areas outside economics reached "ridiculous" conclusions. (wikipedia.org)
  • Exports, economic growth and causality in Korea ," Applied Economics Letters , Taylor & Francis Journals, vol. 12(11), pages 693-696. (repec.org)
  • Causality between export growth and industrial development : Empirial evidence from the NICs ," Journal of Development Economics , Elsevier, vol. 26(1), pages 55-63, June. (repec.org)
  • Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction modelling techniques ," Energy Economics , Elsevier, vol. 18(3), pages 165-183, July. (repec.org)
  • A note on the Hiemstra-Jones test for Granger non-causality ," CeNDEF Working Papers 04-10, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. (repec.org)
  • Testing for Causality-in-Variance: An Application to the East Asian Markets ," International Journal of Finance & Economics , John Wiley & Sons, Ltd., vol. 7(3), pages 235-245, July. (repec.org)
  • Temporal causality and the dynamics of democracy, emigration and real income in Fiji ," International Review of Applied Economics , Taylor & Francis Journals, vol. 19(2), pages 245-261. (repec.org)
  • Government expenditure and economic growth: Evidence from trivariate causality testing ," Journal of Applied Economics , Universidad del CEMA, vol. 8, pages 125-152, May. (repec.org)
  • The book begins by defining the concepts of mechanism and causality in biology and economics, respectively. (springer.com)
  • For example, detection of a causality relationship does not give information on whether a positive or negative shock has significant predictive value. (hindawi.com)
  • and vice versa, the more abstract x or y, the more difficult it is to identify it as cause or effect, but the less difficult it is to ascertain logically its causality or effectiveness. (wikipedia.org)
  • The International Crash of October 1987: Causality Tests ," World Scientific Book Chapters ,in: Economic Uncertainty, Instabilities And Asset Bubbles Selected Essays, chapter 16, pages 251-262 World Scientific Publishing Co. Pte. (repec.org)
  • Natural gas consumption and economic growth: cointegration, causality and forecast error variance decomposition tests for Pakistan ," MPRA Paper 35103, University Library of Munich, Germany, revised 30 Nov 2011. (repec.org)
  • causality question for the relations between vaccines routinely administered to children and several specific adverse events. (nap.edu)
  • Today it is widely believed that any hopes of restoring local causality within a realistic theory have been undermined by Bell's theorem and its supporting experiments. (pitt.edu)