Revisiting Event-Study Designs: Robust and Efficient Estimation

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.

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

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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