This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

Linear panel models, and the “event-study plots” that often accompany them, are popular
tools for learning about policy effects. We discuss the construction of event-study plots and …

We develop a new approach to estimating the causal effects of treatments or instruments
that combine multiple sources of variation according to a known formula. Examples include …

In this survey we discuss the recent causal panel data literature. This recent literature has
focused on credibly estimating causal effects of binary interventions in settings with …

In this article, we study methods for estimating causal effects in settings with panel data,
where some units are exposed to a treatment during some periods and the goal is …

We develop new tools for estimating the causal effects of treatments or instruments that
combine multiple sources of variation according to a known formula. Examples include …

We introduce new inference procedures for counterfactual and synthetic control methods for
policy evaluation. We recast the causal inference problem as a counterfactual prediction and …

We study estimation of causal effects in staggered-rollout designs—that is, settings where
there is staggered treatment adoption and the timing of treatment is as good as randomly …

Staggered adoption of policies by different units at different times creates promising
opportunities for observational causal inference. Estimation remains challenging, however …

Uncertainty quantification is a fundamental problem in the analysis and interpretation of
synthetic control (SC) methods. We develop conditional prediction intervals in the SC …