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Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

Harvard University

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.

Key Words: Bayes • causal inference • Fisher • Neyman • noncompliance • Rubin’s causal model

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