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The difference-in-differences estimator measures the effect of a treatment or policy intervention by comparing change over time of the outcome variable across treatment groups. To interpret the estimate as a causal effect, this strategy requires that, in the absence of the treatment, the outcome variable followed the same trend in treated and untreated groups. This assumption may be implausible if selection for treatment is correlated with characteristics that affect the dynamic of the outcome variable. In this article, I describe the command asdid, which implements the semiparametric difference-in-differences (SDID) estimator of Abadie (2005, Review of Economic Studies 72: 1–19). The SDID is a reweighing technique that addresses the imbalance of characteristics between treated and untreated groups. Hence, it makes the parallel trend assumption more credible. In addition, the SDID estimator allows the use of covariates to describe how the average effect of the treatment varies for different groups of the treated population.

Abadie's Semiparametric Difference-in-differences Estimator