A. Gelman: Causal Inference and Generalizing from Your Data to the Real World

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Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University

Learning from data involves three stages of extrapolation: from sample to population, from treatment group to control group, and from measurement to the underlying construct of interest. Discussions of causal inferences focus on the second of these steps, but all three are important. The first and third steps are relevant for external validity: taking a causal inference from study A and applying it to scenario B. To solve these problems, we use measurement errors, interactions, and multilevel regression and poststratification. We discuss in the context of applied problems in political science, psychology, and pharmacology. This work is in collaboration with Lauren Kennedy.

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