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Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching

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  • Shi, Pengfei
  • Zhang, Xinyu
  • Zhong, Wei

Abstract

We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.

Suggested Citation

  • Shi, Pengfei & Zhang, Xinyu & Zhong, Wei, 2024. "Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching," Economics Letters, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:ecolet:v:238:y:2024:i:c:s0165176524001629
    DOI: 10.1016/j.econlet.2024.111679
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    References listed on IDEAS

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    1. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
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    6. Liu, Chu-An, 2018. "Averaging estimators for kernel regressions," Economics Letters, Elsevier, vol. 171(C), pages 102-105.
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