Aggregating Average Treatment Effects on the Treated in Difference-in-Differences Models

For difference-in-differences methods, there has been great attention to obtaining consistent estimates of treatment effects, especially when the treatment effects are heterogeneous. However, there has been little discussion of the importance of weights in aggregating those treatment effects into an overall average treatment effect on the treated. There are many possible ways to aggregate estimated cohort-time treatment effects. We show that the standard software used to estimate Callaway and Sant’Anna’s method uses weights that are not just the number of treated observations in treated years. Instead, the software uses weights that include the number of observations in the reference pre-period instead of only the number of observations in the treated periods. We discuss why the aggregation weights matter and under what circumstances the weights make the most difference.