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11
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs c ..."
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Cited by 18 (1 self)
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This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Statistical Models for Causation: What Inferential Leverage Do They Provide?” Evaluation Review, 30, 691–713. http://www.stat.berkeley.edu/users/census/oxcauser.pdf
 2008a). “Diagnostics Cannot Have Much Power Against General Alternatives.” http://www.stat.berkeley.edu/users/census/notest.pdf Freedman, D. A. (2008b). “Randomization Does Not Justify Logistic Regression.” http://www.stat.berkeley.edu/users/census/neylog
, 2006
"... Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with littl ..."
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Cited by 11 (4 self)
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Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by “sophisticated” models. This article discusses current models for causation, as applied to experimental and observational data. The intentiontotreat principle and the effect of treatment on the treated will also be discussed. Flaws in perprotocol and treatmentreceived estimates will be demonstrated.
Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies
, 2004
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Confounding Equivalence in Observational Studies (or, when are two measurements equally valuable for effect estimation?)
, 2009
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Confounding Equivalence in Causal Inference
 PROCEEDINGS OF UAI, 433441. AUAI, CORVALLIS, OR, 2010.
, 2010
"... The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e., satisfy the backdoor criteri ..."
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Cited by 1 (1 self)
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The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e., satisfy the backdoor criterion) or (2) the Markov boundaries surrounding the manipulated variable(s) are identical in both sets. Applications to covariate selection and model testing are discussed.
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
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Cited by 1 (0 self)
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We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
Applications and graphics for propensity score analysis*
, 2004
"... Applications and graphics for propensity score analysis Methods for propensity score analysis (PSA) originated with Rosenbaum and Rubin (1983), as vehicles to sharpen and clarify treatment group comparisons in observational studies. Although highly recommended by many statisticians, and applied ofte ..."
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Applications and graphics for propensity score analysis Methods for propensity score analysis (PSA) originated with Rosenbaum and Rubin (1983), as vehicles to sharpen and clarify treatment group comparisons in observational studies. Although highly recommended by many statisticians, and applied often in medical sciences, PSA has seen relatively few applications in the social and behavioral sciences. This paper aims to facilitate sound PSA applications in psychological and other social sciences, and to emphasize the role visualization can play in such contexts. Numerous references to the expanding PSA literature are also provided. 2 Applications and graphics for propensity score analysis
Statistical Models for Causation: A Critical Review
"... Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later ..."
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Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later
Likelihoodbased Causal Inference
, 34
"... A method is given which uses subject matter assumptions to discriminate recursive models and thus point toward possible causal explanations. The assumptions alone do not specify any order among the variables  rather just a theoretical absence of direct association. We show how these assumptions, ..."
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A method is given which uses subject matter assumptions to discriminate recursive models and thus point toward possible causal explanations. The assumptions alone do not specify any order among the variables  rather just a theoretical absence of direct association. We show how these assumptions, while not specifying any ordering, can when combined with the data through the likelihood function yield information about an underlying recursive order. We derive details of the method for multinormal random variables. 4.1 INTRODUCTION Starting from Sewall Wright (1934), directed graphs have been used to represent structures in which variables `cause' or `influence' other variables. Nodes of the graph are used to represent variables and an arrow from one variable to another indicates that the first has a direct causal influence on the second, an influence not blocked by holding constant others considered. If the graphs are restricted to directed acyclic graphs (DAGs) by prohibiting direct...
Confounding Equivalence in Observational Studies Judea Pearl
, 2008
"... Let, and be three disjoint subsets of discrete variables, and their joint distribution. We are concerned with expressions of the type (1) Such expressions, which we name “adjustment estimands. ” are often used to approximate the ..."
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Let, and be three disjoint subsets of discrete variables, and their joint distribution. We are concerned with expressions of the type (1) Such expressions, which we name “adjustment estimands. ” are often used to approximate the