Abadie's semiparametric difference-in-differences estimator
Abstract. 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.
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Kenneth Houngbedji
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absdid, semiparametric estimations, difference-in-differences, propensity score
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