Empirical Strategies in Economics: Illuminating the Path From Cause to Effect
通过特许学校和考试学校出勤效应的实证案例，展示局部平均处理效应（LATE）框架在因果推断中的价值，并强调工具变量排除限制对清晰解释简约形式因果效应的重要性。
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. I also make 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.