Results 1  10
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27
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 25 (8 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.
Identifying linear causal effects
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI
, 2004
"... This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and t ..."
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Cited by 9 (4 self)
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This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
The Foundations of Causal Inference
 SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2010
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of ..."
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Cited by 6 (2 self)
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, 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 the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and
Bayesian inference for Gaussian mixed graph models
 Proceedings of 22nd Conference on Uncertainty in Artificial Intelligence
, 2006
"... We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bidirected edges, is a representation of conditional independencies that is closed under marginalization and arises naturall ..."
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Cited by 5 (3 self)
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We introduce priors and algorithms to perform Bayesian inference in Gaussian models defined by acyclic directed mixed graphs. Such a class of graphs, composed of directed and bidirected edges, is a representation of conditional independencies that is closed under marginalization and arises naturally from causal models which allow for unmeasured confounding. Monte Carlo methods and a variational approximation for such models are presented. Our algorithms for Bayesian inference allow the evaluation of posterior distributions for several quantities of interest, including causal effects that are not identifiable from data alone but could otherwise be inferred where informative prior knowledge about confounding is available. 1
A Criterion for Parameter Identification in Structural Equation Models
, 2007
"... This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper establishes a sufficient criterion for identifying individual causal effects and provides a procedure computing identified causal effects in terms of observed covariance m ..."
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Cited by 4 (1 self)
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This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper establishes a sufficient criterion for identifying individual causal effects and provides a procedure computing identified causal effects in terms of observed covariance matrix.
Measurement bias and effect restoration in causal inference
, 2010
"... This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem o ..."
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Cited by 3 (1 self)
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This paper highlights several areas where graphical techniques can be harnessed to address the problem of measurement errors in causal inference. In particular, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining biasfree effect estimates in such models.
On the identification of a class of linear models
 In Proceedings of the AAAI
, 2007
"... This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper provides a procedure for solving the identification problem in a special class of models. ..."
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Cited by 2 (1 self)
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This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. The paper provides a procedure for solving the identification problem in a special class of models.
Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
"... In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the singledoor criterion, the ins ..."
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Cited by 2 (1 self)
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In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the singledoor criterion, the instrumental variable and the IVpair, and the accessory set. However, there remain graphical models where a direct causal effect can be identified and these graphical criteria all fail. As a result, we introduce a new set of graphical criteria which uses descendants of either the cause variable or the effect variable as “pathspecific instrumental variables ” for the identification of the direct causal effect as long as certain conditions are satisfied. These conditions are based on edge removal and the existing graphical criteria of instrumental variables, and the identifiability of certain other total effects, and thus can be easily checked. 1