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Outcome regression-based estimation of conditional average treatment effect

Author

Listed:
  • Lu Li

    (Shanghai Jiao Tong University)

  • Niwen Zhou

    (Beijing Normal University)

  • Lixing Zhu

    (Beijing Normal University
    Hong Kong Baptist University)

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.

Suggested Citation

  • Lu Li & Niwen Zhou & Lixing Zhu, 2022. "Outcome regression-based estimation of conditional average treatment effect," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 987-1041, October.
    • Handle: RePEc:spr:aistmt:v:74:y:2022:i:5:d:10.1007_s10463-022-00821-x
    DOI: 10.1007/s10463-022-00821-x
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    References listed on IDEAS

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    Cited by:

    1. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
    2. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    3. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
    4. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    5. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    6. Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.
    7. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.

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