Estimating Conditional Average Treatment Effects

  title={Estimating Conditional Average Treatment Effects},
  author={Jason Abrevaya and Yu‐Chin Hsu and Robert P. Lieli},
  journal={Journal of Business \& Economic Statistics},
  pages={485 - 505}
We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the 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… 

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  • M. Frölich
  • Mathematics, Economics
    SSRN Electronic Journal
  • 2002

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