Estimating Conditional Average Treatment Effects
研究了条件平均处理效应（CATE）的估计方法，用于捕捉处理效应在不同子群体中的异质性，并应用于评估孕期吸烟对婴儿出生体重的影响。
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous covariates rather than the quantiles of the potential outcome distributions. We show that the CATE parameter is nonparametrically identified under the unconfoundedness assumption and propose inverse probability weighted estimators for it. Under regularity conditions, some of which are standard and some of which are new in the literature, we show (pointwise) consistency and asymptotic normality of a fully nonparametric and a semiparametric estimator. We apply our methods to estimate the average effect of a firsttime mother's smoking during pregnancy on the baby's birth weight as a function of per capita income in the mother's zip code. For nonwhite mothers, the average effect of smoking is predicted to become stronger (more negative) as a function of income.