Working Paper: NBER ID: w29726
Authors: Joshua Angrist
Abstract: The view that empirical strategies in economics should be transparent and credible now goes almost without saying. The local average treatment effects (LATE) framework for causal inference helped make this so. The LATE theorem tells us for whom particular instrumental variables (IV) and regression discontinuity estimates are valid. This lecture uses several empirical examples, mostly involving charter and exam schools, to highlight the value of LATE. A surprising exclusion restriction, an assumption central to the LATE interpretation of IV estimates, 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; compelling applications demonstrate the power of simple empirical strategies to generate new causal knowledge.
Keywords: Empirical Strategies; Causal Inference; Education; Instrumental Variables; Regression Discontinuity
JEL Codes: B23; I21; I28; J13; J22
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| enrollment at Chicago exam schools (I24) | student achievement (I24) |
| school attendance (I21) | student achievement (I24) |
| peer effects in exam schools (C92) | academic performance for enrolled students (I23) |