Revisiting event study designs: robust and efficient estimation

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects.We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatmenteffect 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 behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions.Extensions include time-varying controls, triple-differences, and certain non-binary treatments.We show the practical relevance…