Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
This work addresses methodological challenges in causal inference for researchers and practitioners analyzing staggered policy adoptions, providing theoretical guarantees for standard estimators.
The paper tackles the problem of estimating average treatment effects in panel data with staggered adoption, showing that under random assignment of adoption dates, the standard Difference-In-Differences estimator is unbiased for a weighted average causal effect and the standard variance estimator is conservative.
In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is is an unbiased estimator of a particular weighted average causal effect. We characterize the proeperties of this estimand, and show that the standard variance estimator is conservative.