Outcome regression-based estimation of conditional average treatment effect

Lu Li, Niwen Zhou, Lixing Zhu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)

Abstract

The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true, parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions when supposing the models are correctly specified, we answer the following questions: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? Meanwhile, the results show that any outcome regression-based estimation should be asymptotically more efficient than any inverse probability weighting-based estimation. Several simulation studies are conducted to examine the finite sample performances of these estimators, and a real dataset is analyzed for illustration.

Original languageEnglish
Pages (from-to)987-1041
Number of pages55
JournalAnnals of the Institute of Statistical Mathematics
Volume74
Issue number5
Early online date29 Apr 2022
DOIs
Publication statusPublished - Oct 2022

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Asymptotic variance
  • Conditional average treatment effect
  • Regression causal effect
  • Sufficient dimension reduction

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