Difference-in-Differences Estimators of Intertemporal Treatment Effects

Understand

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-trends assumption, and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for $\ell$ periods.
• We also propose normalized estimators, that estimate a weighted average of the effects of the current treatment and its lags.