Estimation of average treatment effects in staggered difference-in-differences designs

In this presentation, I discuss the att_gt command, which implements three semiparametric estimators for a family of average treatment-effects parameters in difference-in-differences (DID) setups with multiple time periods discussed in Callaway and Sant’Anna (2020, https://doi.org/10.1016/j.jeconom.2020.12.001). The first estimator models the outcome evolution of the comparison group, the second is a properly reweighted inverse-probability weighted estimator, and the third is a doubly robust estimator that relies on less stringent modeling assumptions. Our implementation allows for the use of different comparison groups (“never-treated” or “not-yet-treated” units) and also allows for limited treatment anticipation. Our inference procedures account for multiple-testing problems. We discuss postestimation approaches that can be used in conjunction with our main implementation. We illustrate the program and provide a simulation study assessing the finite-sample performance of the inference procedures.