  Policy analysts are often interested in treating the units with extreme
outcomes, such as infants with extremely low birth weights. Existing
changes-in-changes (CIC) estimators are tailored to middle quantiles and do not
work well for such subpopulations. This paper proposes a new CIC estimator to
accurately estimate treatment effects at extreme quantiles. With its asymptotic
normality, we also propose a method of statistical inference, which is simple
to implement. Based on simulation studies, we propose to use our extreme CIC
estimator for extreme, such as below 5% and above 95%, quantiles, while the
conventional CIC estimator should be used for intermediate quantiles. Applying
the proposed method, we study the effects of income gains from the 1993 EITC
reform on infant birth weights for those in the most critical conditions. This
paper is accompanied by a Stata command.
