The view that empirical strategies in economics should be transparent and credible now goes almost without saying. By revealing for whom particular instrumental variables (IV) estimates are valid, the local average treatment effects (LATE) framework helped make this so. This lecture uses empirical examples, mostly involving effects of charter and exam school attendance, to illustrate the value of the LATE framework for causal inference. LATE distinguishes independence conditions satisfied by random assignment from more controversial exclusion restrictions. A surprising exclusion restriction is shown to explain why enrollment at Chicago exam schools reduces student achievement. Josh Angrist also makes two broader points: IV exclusion restrictions formalize commitment to clear and consistent explanations of reduced-form causal effects; the credibility revolution in applied econometrics owes at least as much to compelling empirical analyses as to methodological insights.
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