In this paper, we present data visualizations for diagnostic checks on difference-in-differences (DiD) techniques. This approach shows that even in the two-period model without covariates, there is information available to assess the performance of standard DiD approaches. In particular, we show (1) that adding distributional information on the outcomes to standard parallel trends plots can reveal situations where quantile-based changes-in-changes (CiC) approaches (Athey and Imbens 2006) will be preferred to standard DiD and (2) that plots of the distribution of estimated effects can be used to assess robustness of findings for ATT.

In addition, (3) support issues can be addressed through sensitivity analysis based on assumptions about the quantile effects and (4) the ATT can be bracketed by the CIC estimate the support-adjusted estimate. Because parallel trends and time-invariant confounding are not equivalent assumptions on the quantile scale, (5) simultaneous presentation of CiC and reverse CiC allow for a more robust check on DiD when one of these assumptions is not preferred to the other. Since support conditions may not hold for both CiC and reverse CiC, (6) sensitivity analysis based on support violations for both CIC and reverse CIC may produce wide bounds for the ATT.

We illustrate these approaches with two applications: the classic analysis of the effect of raising the minimum wage in NJ (Card and Kreuger 1994) and a recent analysis of the effect of comprehensive judicial reforms on firm productivity (Chemin 2020).