Estimating Conditional Average Treatment Effects
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.
|Date of creation:||20 Jul 2012|
|Date of revision:||20 Jul 2012|
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- Frolich, Markus, 2007.
"Nonparametric IV estimation of local average treatment effects with covariates,"
Journal of Econometrics,
Elsevier, vol. 139(1), pages 35-75, July.
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- Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2012. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," IEAS Working Paper : academic research 12-A017, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Jan 2014.
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- Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 395-415, July.
- Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics, Palgrave Macmillan.
- Abrevaya, Jason & Dahl, Christian M, 2008. "The Effects of Birth Inputs on Birthweight," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 379-397.
- Jason Abrevaya, 2006. "Estimating the effect of smoking on birth outcomes using a matched panel data approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 489-519. Full references (including those not matched with items on IDEAS)