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Statistical Models and Causal Inference by Jasjeet S. Sekhon, David Collier, David A. Freedman

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Editors’ Introduction:Inference and Shoe Leather

David Collier, Jasjeet S. Sekhon, and Philip B. Stark

        Drawing sound causal inferences from observational data is a central goal in social science. How to do so is controversial. Technical approaches based on statistical models—graphical models, non-parametric structural equation models, instrumental variable estimators, hierarchical Bayesian models, etc.—are proliferating. But David Freedman has long argued that these methods are not reliable. He demonstrated repeatedly that it can be better to rely on subject-matter expertise and to exploit natural variation to mitigate confounding and rule out competing explanations.

        When Freedman first enunciated this position decades ago, many ...

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