Revisiting Event-Study Designs: Robust and Efficient Estimation

🤖 Plain-English Summary

Abstract We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show the practical relevance of our results in a simulation study and an application.

🔑 Key Findings

• We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity.
• We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted.
• We characterize the asymptotic behaviour of the estimator, propose tools for inference, and develop tests for identifying assumptions.

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