Results 1  10
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19
From association to causation via regression
 Indiana: University of Notre Dame
, 1997
"... For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend ..."
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Cited by 31 (7 self)
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For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to 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.
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.
Statistical Models for Causation: What Inferential Leverage Do They Provide
 Evaluation Review
, 2006
"... The online version of this article can be found at: ..."
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Cited by 20 (4 self)
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The online version of this article can be found at:
Correcting for omittedvariable and measurementerror bias in autoregressive model estimation with panel data
 Computational Economics
, 2003
"... Abstract. The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the ‘true ’ functional form of the equation is unk ..."
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Cited by 6 (4 self)
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Abstract. The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the ‘true ’ functional form of the equation is unknown. Here, we demonstrate how auxiliary variables, called concomitants, can be used to remove omittedvariable and measurementerror biases from the coefficients of an equation with the unknown ‘true ’ functional form. The method is specifically designed for panel data. Numerical algorithms for enacting this procedure are presented and an illustration is given using a practical example of forecasting smallarea employment from nonlinear autoregressive models. Key words: autoregressive models, omittedvariable biases, measurementerror biases, concomitants, panel data
Prediction and experimental design with graphical causal models
 In Computation, Causation, and Discovery
, 1999
"... Prediction and experimental design with graphical causal models ..."
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Cited by 4 (1 self)
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Prediction and experimental design with graphical causal models
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.
and causal inference in experiments and observational studies
 Statistical Science
, 1990
"... ..."
International Econometric Review (IER) 5 Limits of Econometrics
"... It is an article of faith in much applied work that disturbance terms are IID—Independent and Identically Distributed—across observations. Sometimes, this assumption is replaced by other assumptions that are more complicated but equally artificial. For example, when observations ..."
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It is an article of faith in much applied work that disturbance terms are IID—Independent and Identically Distributed—across observations. Sometimes, this assumption is replaced by other assumptions that are more complicated but equally artificial. For example, when observations
Price in a TimeVarying Environment
, 2009
"... Assessing the Causal Relationship between EuroArea Money and ..."
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