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.
Systems developed for collecting reports from government agencies, manufacturers, hospitals, physicians, and other sources on adverse drug 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.
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.
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.
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.
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.
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.
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.
Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.
Computer-based representation of physical systems and phenomena such as chemical processes.
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.
The scientific discipline concerned with the physiology of the nervous system.
Presentation of pertinent data by one with special skill or knowledge representing mastery of a particular subject.
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 tracts connecting one part of the nervous system with another.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
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.
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.
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.
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.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.
Material prepared from plants.
The process by which the nature and meaning of sensory stimuli are recognized and interpreted.
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.
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.
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.
Elements of limited time intervals, contributing to particular results or situations.
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.
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.
Intellectual or mental process whereby an organism obtains knowledge.
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.
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.
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.
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.
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.
Studies in which variables relating to an individual or group of individuals are assessed over a period of time.
The coordination of a sensory or ideational (cognitive) process and a motor activity.
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.
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.
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.
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.
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.
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.
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.
Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity.
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.
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.
A latent susceptibility to disease at the genetic level, which may be activated under certain conditions.
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)
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.
Use of sophisticated analysis tools to sort through, organize, examine, and combine large sets of information.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
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 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.
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.
Research techniques that focus on study designs and data gathering methods in human and animal populations.
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.
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.
Freedom from activity.
The selecting and organizing of visual stimuli based on the individual's past experience.
The status during which female mammals carry their developing young (EMBRYOS or FETUSES) in utero before birth, beginning from FERTILIZATION to BIRTH.
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.
Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
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.
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.
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.
The nursing of an infant at the breast.
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.
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.
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.
Inhaling and exhaling the smoke of burning TOBACCO.
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.
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.
The time from the onset of a stimulus until a response is observed.
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.
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.
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.
An infant during the first month after birth.
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
Stress wherein emotional factors predominate.
Depressive states usually of moderate intensity in contrast with major depression present in neurotic and psychotic disorders.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
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.
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)
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.
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
Genetic loci associated with a QUANTITATIVE TRAIT.
The awareness of the spatial properties of objects; includes physical space.
Behaviors associated with the ingesting of alcoholic beverages, including social drinking.
Elements of residence that characterize a population. They are applicable in determining need for and utilization of health services.
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 - 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 ...
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.
If Figure 4A represented the truth, then ACR would be an effect mediator, and it would be inappropriate to condition on ACR, because it would block a causal pathway between smoking and ESRD. (If it were done, the effect of this pathway would be removed from the estimate of the total effect of smoking on ESRD.) If Figure 4B represented the truth, however, then ACR would be a collider on one of the two paths between smoking and ESRD, and as such, it would still be inappropriate to adjust for it (because doing so would create a biasing pathway between smoking and ESRD). Specifically, if it were known that someone had an increased level of ACR (the effect of conditioning), then knowing that they were also a smoker would reduce the probability that they have been exposed to the other unknown factors. This is because being a smoker is the more likely cause of their increased value of ACR. In other words, given ACR, smokers will be systematically less likely to have been exposed to the other unknown ...
Causality 3: Itt játszhatod a következőt Causality 3. - A Causality 3 a válogatott Mutass és kattints kalandjáték Játékok egyike.
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The CADDIS web site was updated with new material that was still undergoing final review by U.S. EPA. In addition to this new material, sections of the step by step guide were updated, in particular, the in depth sections, multiple causes, and using statistics responsibly were added. The new sections, listed below, include these general topics: candidate causes, analytical tools, an conceptual models.. Common Candidate Causes: Metals, Sediments, Nutrients, Dissolved Oxygen Temperature, Ionic Strength, Flow Alteration, Unspecified Toxic Chemicals, Interactive Conceptual Model for Phosphorus. Download alert: you will be prompted to download and install the latest version of Flash (a freely available program) in order to view this part of CADDIS.. Analyzing Data: Data Analysis Methods: Scatter Plots, Correlation, Box Plots, Conditional Probability Analysis, Regression Analysis, Predicting Environmental Conditions from Biological Observations, Quantile Regression, Classification and Regression ...
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
This post is part of The Pump Handles Public Health Classics series. By Sara Gorman Does cigarette smoking cause cancer? Does eating specific foods or working
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: NIPS 2016 What If? workshop -…
Heres another free eBook for those looking to up their skills. If you are seeking a resource that exhaustively treats the topic of causal inference, this book has you covered.
Establishing a causal relationship between factors at work and disease is difficult for occupational physicians and researchers. This paper seeks to provide arguments for the judgement of evidence of causality in observational studies that relate work factors to disease. I derived criteria for the judgement of evidence of causality from the following sources: the criteria list of Hill, the approach by Rothman, the methods used by International Agency for Research on Cancer (IARC), and methods used by epidemiologists. The criteria are applied to two cases of putative occupational diseases; breast cancer caused by shift work and aerotoxic syndrome. Only three of the Hill criteria can be applied to an actual study. Rothman stresses the importance of confounding and alternative explanations than the putative cause. IARC closely follows Hill, but they also incorporate other than epidemiological evidence. Applied to shift work and breast cancer, these results have found moderate evidence for a causal ...
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 [email protected] Acknowledgements and references. Feature Extraction, Foundations and Applications I. Guyon, S. Gunn, et al. Springer, 2006. Slideshow...
Andrew,. But not getting results is unhelpful here, right? This is where the p-value thinking hurts in a hidden way - we lose all ability to think about precision as meaningful and useful uncertainty when we think of uncertainty instead as an effect exists or something worked. Im actually not totally sure how similar our thinking is on this. But doesnt this seem more like a case for putting our efforts as much into bounding the possible size of effects as it does about whether or not some regression does or does not produce significant results? That whole bit just needs to disappear. I think we agree up to that point, but how about this as a generic statement of findings for a hypothetical empirical exercise: Our models estimate an effect of a unit change in X on outcome Y of between A and B… then who cares whether there is a zero in between A and B? Just get the best estimates of A and B possible. If you are working in a world where the units dont matter at all or mean ...
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
Hicdep_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
RRH: Rural and Remote Health. Published article number: 6042 - Elders suffering recurrent injurious falls: causal analysis from a rural tribal community in the eastern part of India
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 ...
However, a Interesting Atlas shrugged essay contest information and topic guidelines for College. Explore examples and do correct Atlas shrugged essay contest information and topic guidelines for College. Topics like this make it much easier for a student to make a thesis statement
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 ...
|p|‘The editors of the new |span class=hi-italic|SAGE Handbook of Regression Analysis and Causal Inference|/span| have assembled a wide-ranging, high-qu
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.
Though there isnt yet a clear winner of the presidential election, its hard not to look a little ahead and consider the different potential outcomes. One of these includes the possibility of approximately three months of a lame-duck presidency, if Joe Biden ends up coming out on top.
Realist Evaluation (RE) identifies the causal mechanisms explaining the results of a given intervention in different contexts. Based on the evaluation of the implementation of a health policy in West Africa, this chapter illustrates the different stages of the RE cycle. In the first stage, the research question is formulated. The added value of the RE approach is optimal on questions such as « why, how, for whom, and in what contexts does an intervention work or not? ». The second step is the formulation of the initial program theory, which depends on the nature of the intervention being studied, the objectives, and the nature of the main outputs expected from the evaluation, the resources available and the evaluators familiarity with the object of evaluation The Data collection is method neutral and can be based on a particular form of qualitative interview called « realist interview ». In practice, data collection must adapt to key informants, their perceptions and expectations vis-à-vis ...
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 ...
Ive been feeling very unwell for the past 2 months. My main symptoms are dizzines, nausea, ocasionally im struggling to finish my sentences and my […]
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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.
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Other attempts to define causality include Granger causality, a statistical hypothesis test that causality (in economics) can ... Causality is relevant to the second ladder step. Associations are on the first step and provide only evidence to the latter. A ... Causality is assessed by experimentally performing some action that affects one of the events. Example: if we doubled the price ... They are the highest step on Pearl's causality ladder. Definition: A potential outcome for a variable Y is "the value Y would ...
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). ...
ISBN 3-540-96683-8. p. 4. "Genesis Chapter 1 (KJV)". "Aeonic Theory Of The Order Of Nine Angles , Causality , Civilization". ...
Pearl, Judea (2000). Causality. Cambridge University Press. "Review of the paper: M. L. Ginsberg, "Counterfactuals," Artificial ... Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 978-0-521-77362-1. Thompson, ... Philosophy portal Angelika Kratzer Causality Conditional sentence David Lewis (philosopher) Indicative conditional Modal logic ...
However, a network may accurately embody the Markov condition without depicting causality, in which case it should not be ... In the event that the structure of a Bayesian network accurately depicts causality, the two conditions are equivalent. ... Pearl, Judea (2009). Causality. Cambridge: Cambridge University Press. doi:10.1017/cbo9780511803161. ISBN 9780511803161.. ...
Granger causality can also be used to find the causality between two observational variables under different, but similarly ... Granger causality has been applied to fMRI data. CCD tested their tools using biomedical data [4]. ECA is used in physics to ... Granger causality (there is also the Scholarpedia entry [1]) transfer entropy convergent cross mapping causation entropy PC ... Guo, Ruocheng; Cheng, Lu; Li, Jundong; Hahn, P. Richard; Liu, Huan (2018). "A Survey of Learning Causality with Data: Problems ...
Heckman, J. J. (2008). "Econometric Causality". International Statistical Review. 76 (1): 1-27. doi:10.1111/j.1751-5823.2007. ...
Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 9780521773621. Granger, C. W ... Guo, Ruocheng; Cheng, Lu; Li, Jundong; Hahn, P. Richard; Liu, Huan (2020). "A Survey of Learning Causality with Data". ACM ... David Hume argued that beliefs about causality are based on experience, and experience similarly based on the assumption that ... Exploratory causal analysis, also known as "data causality" or "causal discovery" is the use of statistical algorithms to infer ...
Conditional mutual information Causality Causality (physics) Structural equation modeling Rubin causal model Mutual information ... Massey, James (1990). "Causality, Feedback And Directed Information" (ISITA). CiteSeerX Cite journal requires , ... Hence, it is advantageous when the model assumption of Granger causality doesn't hold, for example, analysis of non-linear ... Hlaváčková-Schindler, Katerina; Palus, M; Vejmelka, M; Bhattacharya, J (1 March 2007). "Causality detection based on ...
The quantum-comb framework also enabled a new understanding of causality in quantum mechanics and quantum field theory. This ... Brukner, Časlav (2014). "Quantum causality". Nature Physics. 10 (4): 259-263. Bibcode:2014NatPh..10..259B. doi:10.1038/ ...
Alfredo Morabia (2005). "Epidemiological causality". History and Philosophy of the Life Sciences. 27 (3-4): 365-79. PMID ... Michael Kundi (July 2006). "Causality and the interpretation of epidemiologic evidence". Environmental Health Perspectives. 114 ...
Causal determinism is the concept that events within a given paradigm are bound by causality in such a way that any state (of ... In 1739, David Hume in his A Treatise of Human Nature approached free will via the notion of causality. It was his position ... In many ways, the Buddhist position is closer to a theory of "conditionality" than a theory of "causality", especially as it is ... Kayser, A.S.; Sun, F.T.; D'Esposito, M. (2009). "A comparison of Granger causality and coherency in fMRI-based analysis of the ...
Farmer, Lindsay (2007). "Complicity beyond causality". Criminal Law and Philosophy. Springer Science+Business Media. 1 (2): 151 ...
Causality before Hume.) Clatterbaugh, Kenneth (July 1998). "What is problematic about 'masculinities'?". Men and Masculinities ... Clatterbaugh, Kenneth; Bobro, Marc (July 1996). "Unpacking the Monad: Leibniz's Theory of Causality". The Monist. 79 (3): 408- ... "Cartesian causality, explanation, and divine concurrence". History of Philosophy Quarterly. University of Illinois Press. 12 (2 ...
One theory states that stable wormholes are possible, but that any attempt to use a network of wormholes to violate causality ... Since the underlying behavior does not violate local causality or allow FTL communication, it follows that neither does the ... A number of authors have published papers disputing Nimtz's claim that Einstein causality is violated by his experiments, and ... Fearn, Heidi (2007). "Can Light Signals Travel Faster than c in Nontrivial Vacuua in Flat space-time? Relativistic Causality II ...
Model specification can be useful determine causality that is slow to emerge, where the effects of an action in one period are ... Notably, correlation does not imply causation, so the study of causality is as concerned with the study of potential causal ... In the 20th century the Bradford Hill criteria, described in 1965 have been used to assess causality of variables outside ... It is worth reiterating that regression analysis in the social science does not inherently imply causality, as many phenomenon ...
Fundamentals of Causality. In: Rouse, W., Boff, K. and Sanderson, P., eds., Complex Socio-Technical Systems: Understanding and ... Causality is one of the key concepts employed in the sciences. In our attempt to understand and influence the world around us, ... Influencing the Causality of Change IOS Press. Mumford, S., 2012. Metaphysics: A Very Short Introduction Oxford University ...
Aristotle on Causality (link to section labeled "Four Causes"). Stanford Encyclopedia of Philosophy 2008. Heidegger, Martin ( ... Indeed, without finality, efficient causality becomes inexplicable. Finality thus understood is not purpose but that end ... "Four Causes , Aristotle on Causality." Stanford Encyclopedia of Philosophy. Lindberg, David. 1992. The Beginnings of Western ... Anthropic principle Biosemiotics Causality Convergent evolution Four discourses, by Jacques Lacan Proximate and ultimate ...
Causality versus interdependence. Macroeconomics before microeconomics. DIsequilibrium and instability (not equilibrium) as the ...
Didelez, Vanessa (2007). "Statistical Causality" (PDF). In Østreng, Willy (ed.). Consilience: Interdisciplinary Communications ...
"Aristotle on Causality". Stanford Encyclopedia of Philosophy. Stanford. Retrieved 2014-03-10.. ...
ISBN 0-262-08271-3. Salmon, Wesley C. (1998). Causality and Explanation. p. 198. ISBN 978-0-19-510864-4. Van Bendegem, Jean ...
Wold's writings on causality and recursive-chain models have been recognized[citation needed] as scientific inventions by ... "Causality and Econometrics," Econometrica, 22(2), p p. 162-177. 1964.Econometric model building : essays on the causal chain ... There is an extensive bibliography published with the ET interview (below). Causality, 2nd ed. "A synthesis of pure demand ... Systems under Indirect Observation: Causality, Structure, Prediction (edited by K. G. Jöreskog and H. Wold), North-Holland. ...
... "computational causality". Using this terminology, it is computational causality, not system causality, that is relevant to the ... "computational causality" is explained using the example of current and voltage in a resistor: "The computational causality of ... over connections between computational causality and system causality. Signal-flow graphs can be used for analysis, that is for ... This point is discussed further in the subsection Interpreting 'causality'. In the most general case, the values for all the xk ...
... "downward causality" manifests, in Hofstadter's view, as the ineffable human instinct that the causality of our minds lies on ... On Downward Causality". I Am a Strange Loop. ISBN 978-0-465-03078-1. Kurt Gödel, 1931, "Über formal unentscheidbare Sätze der ... causality, a situation in which the normal hierarchy of cause-and-effect is flipped upside-down. In the case of Gödel's theorem ...
Kluwer Academic Publishers, Dordrecht/Boston/-London 1997, 460 pp.mISBN 0-7923-4330-1 with Henry Folse (eds.): Causality and ...
Information Causality (IC). The starting point is a bipartite communication scenario where one of the parts (Alice) is handed a ... At the level of correlations between two parties, Einstein's causality translates in the requirement that Alice's measurement ... Pawlowski, M.; Paterek, T.; Kaszlikowski, D.; Scarani, V.; Winter, A.; Zukowski, M. (October 2009). "Information Causality as a ... quantum nonlocality cannot be used to send messages instantaneously and is therefore not in direct conflict with causality ...
"Causality and Freedom". By: Mohsen Araki. Archived from the original on 2014-10-13. Retrieved 2014-10-07. "Imam Abul Qasim al- ... Defending principles of "Sadrian philosophy", with his full support to Sadrian view in the interpretation of causality and its ... he criticized the Sadrian philosophical thought and presented a new viewpoint on the relation between causality and human ... 1375 solar ...
Aristotle on Causality. Stanford Encyclopedia of Philosophy 2008. Aristotle, "Book 5, section 1013a", Metaphysics, Hugh ...
Falcon, Andrea (2019). "Aristotle on Causality". In Zalta, Edward (ed.). Stanford Encyclopedia of Philosophy (Spring 2019 ed ...
Here the notion of causality is one of contributory causality as discussed above: If the true value a j ≠ 0 {\displaystyle a_{j ... Some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that ... The contemporary philosophical literature on causality can be divided into five big approaches to causality. These include the ... Macy, Joanna (1991). "Dependent Co-Arising as Mutual Causality". Mutual Causality in Buddhism and General Systems Theory: The ...
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] R. Gallego, L. E. Würflinger ... Information Causality as a Physical Principle, Nature 461, 1101 (2009). arXiv:0905.2292 [quant-ph] v t e. ...
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 ...
... 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: 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- ...
Tag Archives: causality. Articles and Essays, Philosophy, Physics, Science, Unpublished Light Travel Time Effects and ... This reversal of causality has implications in every facet of our existence, all the way up to our notion of free will. ... But there is a curious inversion of logic, or reversal of causality in the theory of evolution. This is almost the opposite of ... causalitycosmic microwave backgroundexpanding universegamma ray burstsgrblight travel timemicrowave background radiation ...
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 ...
Causality, Meaningful Complexity and Embodied Cognition. Editors. * A. Carsetti Series Title. Theory and Decision Library A:. ...
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 ...
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 ...
This research topic will review and further explore the nature of the mutual influence between time and causality, how causal ... and suggests that our representations of time and causality are mutually influencing one another. At present, the theoretical ...
... J Steroid Biochem Mol Biol. 2018 Jan;175:29-43. doi: 10.1016/j.jsbmb.2016.12. ...
38 (last row), along with the true causality maps (first row). Each panel represents the estimated 8. ×. 8. causality map at a ... Extracting neuronal functional network dynamics via adaptive Granger causality analysis. Alireza Sheikhattar, Sina Miran, Ji ... Extracting neuronal functional network dynamics via adaptive Granger causality analysis. Alireza Sheikhattar, Sina Miran, Ji ... 2011) A Granger causality measure for point process models of ensemble neural spiking activity. PLoS Comput Biol 7:e1001110. ...
Explore causality profile at Times of India for photos, videos and latest news of causality. Also find news, photos and videos ... causality News: Latest and Breaking News on causality. ... Delhi: Fire breaks out in four chemical factories, no causality ... Fire breaks out in slum cluster in Sahibabad, shanties gutted but no causality ...
The direction of causality, however, is unclear. We use employment data from the ES202 program and prices from the American ... Obesity and Depression: Establishing Causality. iHEA 2007 6th World Congress: Explorations in Health Economics Paper ... Colman, Greg and Kelly, Inas, Obesity and Depression: Establishing Causality. iHEA 2007 6th World Congress: Explorations in ...
1 Receiving Gods Blessings with Awareness When a blessing (nima) comes to you from an apparent outward entity, do notreceive that blessing in t...
Bohm (ibid., p. 95) took the view that the abandonment of causality had been too hasty: it is quite possible that while the ... Bohm, D. (1984). Causality and Chance in Modern Physics. London: Routledge & Kegan Paul. First published in 1957. ...
... causality and (ii) to analyze how the dynamics of the system are represented in the GG-causality measure. ... Reply to Barnett et al.: Regarding interpretation of Granger causality analyses Message Subject (Your Name) has sent you a ... 1) point out that the issues with bias and variance in the conditional GG causality can be addressed using a state-space ... Reply to Barnett et al.: Regarding interpretation of Granger causality analyses. Patrick A. Stokes and Patrick L. Purdon ...
... evidence and causality. [Kathleen R Stratton; Institute of Medicine (U.S.). Committee to Review Adverse Effects of Vaccines.] ... Causality. a schema:Intangible ;. schema:name "Causality"@en ;. .. ... Adverse effects of vaccines : evidence and causality. 作者:. Kathleen R Stratton; Institute of Medicine (U.S.). Committee to ... schema:name "Adverse effects of vaccines : evidence and causality"@en ;. schema:productID "778040126" ;. schema:publication < ...
Modeling Causality for Pairs of Phenotypes in System Genetics. Elias Chaibub Neto, Aimee T. Broman, Mark P. Keller, Alan D. ... Modeling Causality for Pairs of Phenotypes in System Genetics. Elias Chaibub Neto, Aimee T. Broman, Mark P. Keller, Alan D. ... Modeling Causality for Pairs of Phenotypes in System Genetics. Elias Chaibub Neto, Aimee T. Broman, Mark P. Keller, Alan D. ... Modeling Causality for Pairs of Phenotypes in System Genetics Message Subject (Your Name) has forwarded a page to you from ...
3 4 Resolving the question of causality is important because serum homocysteine can be lowered by the B vitamin folic acid, 5 6 ... Interpretation of evidence for causality. The results of the MTHFR and the prospective studies can be explained in one of two ... Homocysteine and cardiovascular disease: evidence on causality from a meta-analysis BMJ 2002; 325 :1202 ... We assess the evidence for causality between serum homocysteine and ischaemic heart disease, deep vein thrombosis with or ...
  • The truth is that you need to do more than just show a correlation of occurances for causality. (
  • 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. (
  • We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. (
  • Often causality is confused with correlation. (
  • Human intuition has evolved such that it has learned to identify causality through correlation. (
  • In this book, four main themes are considered and these are causality, correlation, artificial intelligence. (
  • All this shows is that correlation is not causality - just because we can see that two things vary together it doesn't mean we can safely infer that one is causing the other. (
  • Correlation on its own proves nothing - even if you can establish that correlation exists you still have to figure out the direction of causality. (
  • Getting the direction of causality wrong can play havoc with correlation related decisions. (
  • In the world of stocks and assets it's not easy to tease apart the issues of correlation and causality and it's also extremely dangerous to build an investment approach on the basis that assets that historically aren't correlated can never be so. (
  • But he did not have the understanding that came with knowledge of Minkowski geometry and the special theory of relativity , that the notion of causality can be used as a prior foundation from which to construct notions of time and space. (
  • The notion of causality is so central to our ideas about scientific predictions, that it is seems unavoidable. (
  • In this module, we want to talk about reverse causality, which is especially a problem if we're dealing with cross-sectional observational data. (
  • Reverse causality is most commonly a concern with cross-sectional studies. (
  • At least we don't have to worry about the possibility that it's reverse causality at work. (
  • But again, we don't necessarily have to worry that it's reverse causality. (
  • What do we do about reverse causality? (
  • Trust and health: testing the reverse causality hypothesis. (
  • However, as the reverse causality hypothesis has yet to be empirically tested, a knowledge gap remains. (
  • author = {Giordano, Giuseppe Nicola and Lindström, Martin}, issn = {1470-2738}, language = {eng}, month = {11}, publisher = {BMJ Publishing Group}, series = {Journal of Epidemiology and Community Health}, title = {Trust and health: testing the reverse causality hypothesis. (
  • importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. (
  • In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness. (
  • 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. (
  • 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). (
  • This paper compares the statistical properties of time-varying causality tests when errors of variables have multivariate stochastic volatility (SV). (
  • To really know what caused an event, we need to look at causality: how information flows from one event to another. (
  • Why look at causality in the sciences? (
  • The direction of causality, however, is unclear. (
  • For the physical definition of "causality", see Causality (physics) . (
  • Hume interpreted the latter as an ontological view, i.e., as a description of the nature of causality but, given the limitations of the human mind, advised using the former (stating, roughly, that X causes Y if and only if the two events are spatiotemporally conjoined, and X precedes Y) as an epistemic definition of causality. (
  • The classical definition of causality states that a cause and its effect(s) can be different types of entity. (
  • The definition of causality on a discrete space-time assumes that space-time is made up of geometrical points. (
  • Company executives hire process-engineering professionals to accurately identify causality so that harmful problems are not perpetuated. (
  • 1] [2] and [3] proposed nonlinear causality tests. (
  • Their analyses also showed significant nonlinear causality. (
  • The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. (
  • The estimator was shown to capture linear and nonlinear causality on artificial and physiological data (cardiovascular interactions). (
  • 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. (
  • 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. (
  • Causality between export growth and industrial development : Empirial evidence from the NICs ," Journal of Development Economics , Elsevier, vol. 26(1), pages 55-63, June. (
  • Microbiota dysbiosis in select human cancers: Evidence of association and causality. (
  • Chontanawat J, Hunt LC, Pierse R (2006) Causality between energy consumption and GDP: evidence from 30 OECD and 78 non-OECD countries. (
  • For example, [4] provided Monte Carlo evidence that causality tests have size distortions under heteroskedastic variances. (
  • Our simulation results provide evidence that asymptotic time-varying causality tests and their counterparts with HCCME over-reject the null hypothesis of no causality in the presence of SV. (
  • Evidence for causality of rare variants based on exact sharing probabilities in affected relatives. (
  • For example, epidemiological notions of causality are difficult to reconcile with legal notions of causality, in part because the former offers population-based evidence of a general nature, and the later requires individual-based evidence of a specific nature. (
  • Zika virus and neurological disease: is there evidence for causality? (
  • Zika Virus and Birth Defects - Reviewing the Evidence for Causality. (
  • Strength of evidence for their causality depends not only on the penetrance but also family size and structure (number of informative meiosis). (
  • 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. (
  • You just viewed 9.916-A Probability and Causality in... . (
  • There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. (
  • Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. (
  • On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. (
  • These disciplines have developed very different methods, where causality and probability often seem to have different understandings, and where the mechanisms involved often look very different. (
  • A robust causality assessment method (CAM) is not only indispensable for the diagnosis of suspected drug-induced liver injury (DILI) and herb-induced liver injury (HILI) but is also critical for the investigation of the clinical features, risk factors, and incidence in pharmacological or epidemiological studies. (
  • There have however been concerns about attributing causality based on epidemiological data alone and a recent paper in the New England Journal of Medicine noted that there were methodological concerns in some of the studies and that it may be premature to invoke causality [1]. (
  • Some writers have held that causality is metaphysically prior to notions of time and space . (
  • Kant thought that time and space were notions prior to human understanding of the progress or evolution of the world, and he also recognized the priority of causality. (
  • In addition to providing more generic, compact descriptions, this also enables completely new analysis techniques based on notions of causality familiar from concurrency theory. (
  • The answers have come up, as there has been a change in perception about the three core notions, essential for wellness and goodness.These three notions are called 3Cs - Cognition, Consciousness and Causality. (
  • RUCAM (Roussel Uclaf Causality Assessment Method) is the most widely used CAM in suspected DILI and HILI cases worldwide, as evidenced by its application in a large number of case reports and case series since RUCAM was first published in 1993. (
  • Hayashi PH (2016) Drug-induced Liver Injury Network causality assessment: criteria and experience in the United States. (
  • Formally, causality quantifies interactions between variables and identifies cause-effect relationships through modeling, prediction and assessment of the goodness-of-fit when past information from one variable (cause) are incorporated into the prediction of another variable (effect). (
  • Byakika-Tusiime, J. (2008) Circumcision and HIV infection Assessment of causality. (
  • The Ministry of Health of Oman, in close collaboration with WHO, hosted a WHO regional training workshop on revised WHO methodology for causality assessment of adverse events following immunization in Muscat, Oman, on 23-26 June 2014. (
  • The contemporary philosophical literature on causality can be divided into five big approaches to causality. (
  • 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. (
  • Causality and Prediction Mar. 21, 2019 2:30 PM - 3:30 PM , Domus Medica (2nd floor): 'new' meeting room at the Dept. of Biostatistics. (
  • 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. (
  • In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments. (
  • In contrast, we find that time-varying causality tests using wild bootstrap have reasonable empirical sizes and sufficient power. (
  • The empirical findings show that there are bi-directional dynamic causality relationships between openness and indigenous factors. (
  • Causality (also referred to as causation , [1] or cause and effect ) is influence by which one event, process or state, a cause , contributes to the production of another event, process or state, an effect , [2] where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. (
  • Causality (also referred to as causation , [1] or cause and effect ) is what connects one process (the cause ) with another process or state (the effect ), [ citation needed ] where the first is partly responsible for the second, and the second is partly dependent on the first. (
  • Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. (
  • 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. (
  • 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). (
  • C NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). (
  • 2008a ) proposed a causality estimator based on nonlinear exogenous autoregressive (NARX) modeling. (
  • This paper proposes a nonparametric test of Granger causality in quantile. (
  • 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. (
  • 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. (
  • 2014 Exploratory modeling: extracting causality from complexity. (
  • p. 95) took the view that the abandonment of causality had been too hasty: 'it is quite possible that while the quantum theory, and with it the indeterminacy principle, are valid to a very high degree of approximation in a certain domain, they both cease to have relevance in new domains below that in which the current theory is applicable. (
  • This text authored by Mohsen Mohammadi Araghi which addresses the topics of freewill and causality in contemporary Islamic and western philosophy and the theory of moral obligation. (
  • 3 4 Resolving the question of causality is important because serum homocysteine can be lowered by the B vitamin folic acid, 5 6 raising the prospect of a simple and safe means of prevention. (
  • This confusion is partially because causality flows from aerosol to clouds and clouds to aerosol, and it is hard to tell what is happening in observations. (
  • The precise, objective reality of engineering causality can be demanding. (
  • This may be enough for some, but it still does not prove human causality. (
  • No contemporary guide exists for using statistics to prove causality in court. (
  • We conclude that when time series are short, with their lengths shorter than the number of variables, sparse models are better suited to uncover true causality links with LoGo retrieving the true causality network more accurately than Glasso and ridge. (
  • These features are the outworking of systems with intrinsic sensors and programmed logic that are accurately described with engineering causality-which is characterized as internal to them. (
  • The main points of our work were ( i ) to characterize statistical properties of the traditional computation of Granger-Geweke (GG) causality and ( ii ) to analyze how the dynamics of the system are represented in the GG-causality measure. (
  • In this paper, time series statistics from 1980 to 2017 will be used to analyze the relationship between real GDP per capita and energy consumption to will examine how energy use in the country affects economic growth using causality models. (
  • Granger causality is one of most representative methods to analyze causality between economic variables. (
  • Costatini V, Mattini C (2009) The causality between energy consumption and economic growth: a multi-sectoral analysis using non stationary cointegrated panel data. (
  • 7. Causes of changes in X, need not be causes of X. That's often obvious in known-causality cases (pills lowering cholesterol aren't its cause) but routinely obfuscated in analysis-of-variance research . (
  • While time-varying causality is significant for the precise analysis of variables, heteroskedastic variances influence the tests for causality and nonlinearity such as time- varying properties. (
  • Principal Component Analysis (PCA) was applied to reduce data dimension and Granger causality and divergence techniques were applied to analyse connectivity along the atria, in three main regions: pulmonary veins, left atrium (LA) and right atrium (RA). (
  • More than 20 participants from various countries throughout the Eastern Mediterranean Region were in Oman to build their skills in the detection and investigation of adverse events using the new WHO methodology for assessing the causality of adverse events following immunization. (
  • Causality is an abstraction that indicates how the world progresses, [ citation needed ] so basic a concept that it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. (
  • Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). (
  • One viewpoint on this question is that cause and effect are of one and the same kind of entity, with causality an asymmetric relation between them. (
  • It performs Granger-causality tests and STAR-EXT estimation to assess the causality direction and the nonlinear nature of the relation for a set of OECD countries. (
  • Design of clinical research whose purpose is to answer questions about causality can be classified in relation to four axes: the number of study groups, the implementation of an experimental maneuver, cause-effect directionality and source from which the data are collected. (
  • 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. (
  • Models for simultaneous causality are developed. (
  • Bivariate VAR models to test Granger causality between tourist demand and supply: Implications for regional sustainable growth ," Papers in Regional Science , Wiley Blackwell, vol. 88(1), pages 231-244, March. (
  • Mathematical models for general causality have been very limited, working for up to two causes. (
  • Granger causality is quantified from the goodness-of-fit of Autoregressive models fitted onto the effect on its own (univariate model), and fitted onto the effect and the cause together (bivariate model). (
  • For these three methodologies we explore the regions of time series lengths and model-parameters where a significant fraction of true causality links is retrieved. (
  • 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. (
  • 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. (
  • Causality is the result of the concept of time. (
  • and since causality (including time and space) just is a series of relations between things. (
  • The compared time-varying causality tests include asymptotic tests, heteroskedasticity-robust tests, and tests using wild bootstrap. (
  • In particular, the time-varying causality test with first-order Taylor approximation using wild bootstrap has better statistical properties. (
  • This implies that a causality relationship can also be time-varying, and hence we should take into account the time-varying properties when analyzing a causality relationship. (
  • One method to introduce time-varying properties to Granger causality is through the use of a logistic smooth transition (LST) function. (
  • By using an LST function with time as the transition variable, we can test for both smooth and abrupt causalities. (
  • Therefore, if we do not deal appropriately with heteroskedastic variances in the tests for causality, we would not be able to obtain reliable results when examining for time-varying causality. (
  • However, previous studies have not clarified the influences of heteroskedastic variances on time-varying causality tests. (
  • This paper investigates the statistical properties of time-varying causality tests when the disturbance terms have SV. (
  • We reveal the impact of the order of Taylor approximation on time- varying causality tests in the presence of SV. (
  • We also examine the time-varying causality tests using wild bootstrap. (
  • The results of this paper would enable appropriate and reliable time-varying causality tests. (
  • One powerful approach to identifying causal connectivity from time series data, originally developed in the 1960s by Norbert Wiener and Clive Granger, is 'Granger causality' (GC) [4] , [5] . (
  • - Causality has been a subject of study for a long time. (
  • This study provides insights and information on reversed causality, namely, the effects that engagement and burnout can have over time. (
  • On the other end of the genetic causality spectrum, the vast majority of genetic variants that exhibit low penetrance and exert negligible or modest effect sizes (2,7) . (
  • Experimental test of nonlocal causality. (
  • We evaluate the performance of our tests against the AIC, BIC, and a recently published causality inference test in simulation studies. (
  • We extend Zheng's approach to the case of dependent data, particularly to the test of Granger causality in quantile. (
  • However, persuading non-statisticians about causality poses some distinct challenges, because lay audiences are prone to characteristic errors of judgment and inference wherever statistics is involved, as Kahneman's research shows[4]. (