A student and I were discussing what descriptive statistics to report in a regression table—alongside effect estimates from a DID study—to help readers better interpret the findings. As an analogy, in a randomized experiment we often report the mean of the control group to contextualize the treatment effect estimate (e.g., to interpret it as a percentage change or in terms of outcome levels).
We noted that reporting either the pre- or post-treatment *control* group mean does not make much sense for a standard DID targeting the ATT, since the levels of the control group outcomes have no particular relevance to the magnitude of the treatment effect. In the toy example below, for instance, the magnitude of the estimated treatment effect (the ATT) is larger than both the pre- and post-treatment control group means, even though the outcomes are restricted to be positive. But this is not, in itself, a problem.
For a standard DID study targeting an ATT, it seems most appropriate to report the *treated group’s post-treatment* mean. What you would be saying is: “Here is what we observe for the treated group. The ATT tells us how the observed value compares to what would have happened, counterfactually, had the treatment not been applied.”
In the toy sketch, if we were reporting event-study estimates, then for period 3 we would report the treated group mean of zero alongside the ATT estimate of −8. This communicates that we observe a level of 0 for the treated group, and that this is 8 units lower than what we would have observed absent treatment. If instead we were pooling the post-treatment periods, we would report the treated group mean of 2/3 alongside the ATT estimate of -6.67 (the average of the period-specific ATTs for periods 1, 2, and 3). For a staggered DID, if the estimator averages over cohorts, one could similarly report the average post-treatment treated-group mean across cohorts.
Of course, one could also report the implied counterfactual mean—i.e., what would have happened in the absence of treatment—along with the ATT. This gives an interpretation closer to what we are used to seeing from randomized experiments. I prefer reporting the observed treated-group mean with the ATT, however, because it presents an actually observed quantity (subject to sampling of course) alongside a model-based estimate. This also has a practical benefit: in robustness checks with different model specifications, the treated-group mean remains fixed while the estimated ATTs vary, providing a stable reference point. The counterfactual mean, by contrast, would vary with each specification.
This is all fairly simple, but I don’t think I have often seen people report the observed post-treatment treated-group mean in DID regression tables. If you know of examples, I’d be happy to see them.