Efficient semiparametric estimation of network treatment effects under partial interference

Multivariate binary discrimination by the kernel method

C Park and H Kang

Biometrika, 2022, vol. 109, issue 4, 1015-1031

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.

Keywords: Direct effect; Indirect effect; Partial interference; Semiparametric efficiency (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asac009 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:109:y:2022:i:4:p:1015-1031.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().