Knowledge of causal effects is of great importance to decision makers in a wide variety of
settings. In many cases, however, these causal effects are not known to the decision makers …

Difference-in-difference (DID) estimators are a valuable method for identifying causal effects
in the public health researcher's toolkit. A growing methods literature points out potential …

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 regressions with period and group fixed effects are widely used to estimate policie's
effects: 26 of the 100 most cited papers published by the American Economic Review from …

A recent literature has shown that when adoption of a treatment is staggered and average
treatment effects vary across groups and over time, difference-in-differences regression …

This paper proposes tools for robust inference in difference-in-differences and event-study
designs where the parallel trends assumption may be violated. Instead of requiring that …

We explain when and how staggered difference-in-differences regression estimators,
commonly applied to assess the impact of policy changes, are biased. These biases are …

We study treatment-effect estimation using panel data. The treatment may be non-binary,
non-absorbing, and the outcome may be affected by treatment lags. We make a parallel …

Synthetic control methods are commonly applied in empirical research to estimate the
effects of treatments or interventions on aggregate outcomes. A synthetic control estimator …

Many studies estimate the impact of exposure to some quasiexperimental policy or event
using a panel event study design. These models, as a generalized extension of “difference …