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Causal inference in statistics: An Overview
, 2009
"... This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all ca ..."
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Cited by 68 (11 self)
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This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects ” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret, ” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potentialoutcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
Probabilities of Causation: Bounds and Identification
 Annals of Mathematics and Artificial Intelligence
, 2000
"... This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show h ..."
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Cited by 22 (10 self)
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This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to bound these quantities from data obtained in experimental and observational studies, under general assumptions concerning the datagenerating process. In particular, we strengthen the results of Pearl (1999) by presenting sharp bounds based on combined experimental and nonexperimental data under no process assumptions, as well as under the mild assumptions of exogeneity (no confounding) and monotonicity (no prevention). These results delineate more precisely the basic assumptions that must be made before statistical measures such as the excessriskratio could be used for assessing attributional quantities such as the probability of causation. 1
Causality in the Social and Behavioral Sciences
 A PAPER SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2009
"... This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illust ..."
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Cited by 2 (1 self)
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This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illustrated using a general theory of causation based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, which provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potentialoutcome frameworks, and develops symbiotic tools that use the strong features of both.
HEALTH LAW AND ETHICS Inconsistency in Evidentiary Standards for Medical Testimony Disorder in the Courts
"... THE SUPREMECOURT, BASED ON 3 DECISIONS OVER THEpast decade, now requires judges to examine the underlying basis of all testimony to ensure that onlyexpert testimony supported by validmethods of inquiry is introduced as evidence in litigation.1 Under these standards, expert testimony in the courtro ..."
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THE SUPREMECOURT, BASED ON 3 DECISIONS OVER THEpast decade, now requires judges to examine the underlying basis of all testimony to ensure that onlyexpert testimony supported by validmethods of inquiry is introduced as evidence in litigation.1 Under these standards, expert testimony in the courtroom, including medical testimony, is supposed to meet the same standards of intellectual rigor that professionals use outside the courtroom.2 If expert testimony does not meet this standard, the courts are expected to exclude the testimony and may dismiss the case without trial. Yet this new, closer scrutiny by judges has also yielded inconsistent legal decisions in otherwise similarmedical cases that involve injury from putatively toxic substances including drugs (socalled toxic tort cases). In some instances, judges have excluded medical testimony on causeandeffect relationships unless it is based on published, peerreviewed, epidemiologically sound studies, even thoughprac
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"... This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structuralsemantical de nitions of the probabilities of necessary or su cient causation (or both), we show how to optimally bound these quantities from data obtained in e ..."
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This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structuralsemantical de nitions of the probabilities of necessary or su cient causation (or both), we show how to optimally bound these quantities from data obtained in experimental and observational studies, making minimal assumptions concerning the datagenerating process. In particular, we strengthen the results of Pearl (1999) by weakening the datageneration assumptions and deriving theoretically sharp bounds on the probabilities of causation. These results delineate precisely how empirical data can be used both in settling questions of attribution and in solving attributionrelated problems of decision making. 1
Probabilities
"... This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show how t ..."
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This paper deals with the problem of estimating the probability of causation, that is, the probability that one event was the real cause of another, in a given scenario. Starting from structuralsemantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to bound these quantities from data obtained in experimental and observational studies, under general assumptions concerning the datagenerating process. In particular, we strengthen the results of Pearl [39] by presenting sharp bounds based on combined experimental and nonexperimental data under no process assumptions, as well as under the mild assumptions of exogeneity (no confounding) and monotonicity (no prevention). These results delineate more precisely the basic assumptions that must be made before statistical measures such as the excessriskratio could be used for assessing attributional quantities such as the probability of causation. 1.
Epidemiology in History Hume, Mill, Hill, and the Sui Generis Epidemiologic Approach to Causal Inference
, 2012
"... The epidemiologic approach to causal inference (i.e., Hill’s viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It ..."
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The epidemiologic approach to causal inference (i.e., Hill’s viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It does not involve statements of statistical significance. The philosophical roots of Hill’s viewpoints are unknown. Superficially, they seem to descend from the ideas of Hume and Mill. Hill’s viewpoints, however, use a different kind of evidence and have different purposes than do Hume’s rules or Mill’s system of logic. In a nutshell, Hume ignores comparative evidence central to Hill’s viewpoints. Mill’s logic disqualifies as invalid nonexperimental evidence, which forms the bulk of epidemiologic findings reviewed from Hill’s viewpoints. The approaches by Hume and Mill cannot corroborate successful implementations of Hill’s viewpoints. Besides Hume and Mill, the epidemiologic literature is clueless about a plausible, pre1965 philosophical origin of Hill’s viewpoints. Thus, Hill’s viewpoints may be philosophically novel, sui generis, still waiting to be validated and justified. causality; David Hume; evidence; inference; John Stuart Mill; lung cancer; philosophy; tobacco The epidemiologic approach to causal inference consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observa