  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. We also analyze commonly-used
two-way-fixed-effects regressions. Unlike our estimators, they can be biased in
the presence of heterogeneous treatment effects. A local-projection version of
those regressions is biased even with homogeneous effects.
