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Efficient semiparametric estimation of network treatment effects under partial interference
[Multivariate binary discrimination by the kernel method]

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  • C Park
  • H Kang

Abstract

SummaryAlthough many estimators for network treatment effects have been proposed, their optimality properties, in terms of semiparametric efficiency, have yet to be resolved. We present a simple yet flexible asymptotic framework for deriving the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one existing estimator, that proposed by Liu et al. (2019), is locally efficient. We also present other estimators that are efficient and discuss results on adaptive estimation. We illustrate application of the efficient estimators in a study of the direct and spillover effects of conditional cash transfer programmes in Colombia.

Suggested Citation

  • C Park & H Kang, 2022. "Efficient semiparametric estimation of network treatment effects under partial interference [Multivariate binary discrimination by the kernel method]," Biometrika, Biometrika Trust, vol. 109(4), pages 1015-1031.
    • Handle: RePEc:oup:biomet:v:109:y:2022:i:4:p:1015-1031.
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    File URL: http://hdl.handle.net/10.1093/biomet/asac009
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    Cited by:

    1. Yi Zhang & Kosuke Imai, 2023. "Individualized Policy Evaluation and Learning under Clustered Network Interference," Papers 2311.02467, arXiv.org, revised Feb 2024.

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