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From association to causation: Some remarks on the history of statistics
 Statist. Sci
, 1999
"... The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More ..."
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Cited by 23 (6 self)
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The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More recently, investigators in the social and life sciences have used statistical models and significance tests to deduce causeandeffect relationships from patterns of association; an early example is Yule’s study on the causes of poverty (1899). In my view, this modeling enterprise has not been successful. Investigators tend to neglect the difficulties in establishing causal relations, and the mathematical complexities obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Snow’s work on cholera will be contrasted with modern studies that depend on statistical models and tests of significance. The examples may help to clarify the limits of current statistical techniques for making causal inferences from patterns of association. 1.
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 16 (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.
Causation, Statistics, and Sociology
 EUROPEAN SOCIOLOGICAL REVIEW,VOL. 17 NO. 1, 1^20
, 2001
"... Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as ..."
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Cited by 7 (0 self)
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Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as the basis for causal analysis in sociology. It allows the respective roles of statistics and theory to be clarified and is appropriate to sociology as a largely nonexperimental social science in which the concept of action is central.
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
A philosophical investigation of causal interpretation in structural equation models
, 2002
"... This paper is a brief overview and evaluation of current mathematical/statistical causal models, including the structural equation model (SEM), TETRAD, and the graphical model. The efficacy of these approaches will be discussed in the philosophical context of the DuhemQuine thesis, realism, simpl ..."
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This paper is a brief overview and evaluation of current mathematical/statistical causal models, including the structural equation model (SEM), TETRAD, and the graphical model. The efficacy of these approaches will be discussed in the philosophical context of the DuhemQuine thesis, realism, simplicity, identifiability (testability), empirical adequacy, and probabilistic causality. The emphasis of this paper is on the philosophical aspect, not the mathematical or computational aspect of SEM, nonetheless, readers are not required to have a philosophical background to follow the arguments.
TETRAD and SEM
"... refore to elaborate some additional rationale for faithfulness. Because TETRAD relies primarily on nonexperimental data, causal claims are issued with guarantees weaker than those obtained through controlled randomized experiments. Pearl and Verma (1991) expressed these guarantees in terms of two or ..."
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refore to elaborate some additional rationale for faithfulness. Because TETRAD relies primarily on nonexperimental data, causal claims are issued with guarantees weaker than those obtained through controlled randomized experiments. Pearl and Verma (1991) expressed these guarantees in terms of two orthogonal notions: minimality and stability. 2 Minimality guarantees that any alternative structure compatible 1 Causal independence, also known as "Reichenbach's Principle" or "no correlation without causation," is rooted in the principle of no action at a distance [Arntzenius, 1990]. The notion of faithfulness (also called "DAGisomorphism" and "nondegeneracy" [Pearl, 1988, p. 391]) was termed "stability" by Pearl and Verma (1991) to emphasize the invariance of certain independencies to functional form. 2 These guarantees were advanced in connection with the discovery algorithm IC (for "Inferred Causation") which was developed in parallel with and
QUALITY OF PUBLIC FINANCES AND GROWTH 1
, 2005
"... In 2005 all ECB publications will feature a motif taken from the €50 banknote. This paper can be downloaded without charge from ..."
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In 2005 all ECB publications will feature a motif taken from the €50 banknote. This paper can be downloaded without charge from