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31
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 36 (7 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 29 (2 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.
The path analysis controversy: A new statistical approach to strong apWALLER AND MEEHL336 praisal of verisimilitude
 Psychological Methods
, 2002
"... A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The ..."
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Cited by 17 (4 self)
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A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The observed correlation matrix is partitioned into disjoint sets. One set is used to estimate the model parameters, and a nonoverlapping set is used to assess the model’s verisimilitude. Computer code was written to generate competing models and to test the conjectured model’s superiority (relative to the generated set) using diagram combinatorics and is available on the Web
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 16 (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.
The Intervention Selection Bias: An Underrecognized Confound in Intervention Research
"... Selection bias can be the most important threat to internal validity in intervention research, but is often insufficiently recognized and controlled. The bias is illustrated in research on parental interventions (punishment, homework assistance); medical interventions (hospitalization); and psycholo ..."
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Cited by 2 (0 self)
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Selection bias can be the most important threat to internal validity in intervention research, but is often insufficiently recognized and controlled. The bias is illustrated in research on parental interventions (punishment, homework assistance); medical interventions (hospitalization); and psychological interventions for suicide risk, sex offending, and juvenile delinquency. The intervention selection bias is most adequately controlled in randomized studies or strong quasiexperimental designs, although recent statistical innovations can enhance weaker designs. The most important points are to increase awareness of the intervention selection bias and to systematically evaluate plausible alternative explanations of data before making causal conclusions. The difficulty of making causal conclusions from nonrandomized studies is widely recognized by methodologists (e.g., Copas &
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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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.
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