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On inverse probability-weighted estimators in the presence of interference

Author

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  • L. Liu
  • M. G. Hudgens
  • S. Becker-Dreps

Abstract

We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual’s treatment affects the outcome of another individual. In the observational setting where the treatment assignment mechanism is not known, inverse probability-weighted estimators have been proposed when individuals can be partitioned into groups such that there is no interference between individuals in different groups. Unfortunately this assumption, which is sometimes referred to as partial interference, may not hold, and moreover existing weighted estimators may have large variances. In this paper we consider weighted estimators that could be employed when interference is present. We first propose a generalized inverse probability-weighted estimator and two Hájek-type stabilized weighted estimators that allow any form of interference. We derive their asymptotic distributions and propose consistent variance estimators assuming partial interference. Empirical results show that one of the Hájek estimators can have substantially smaller finite-sample variance than the other estimators. The different estimators are illustrated using data on the effects of rotavirus vaccination in Nicaragua.

Suggested Citation

  • L. Liu & M. G. Hudgens & S. Becker-Dreps, 2016. "On inverse probability-weighted estimators in the presence of interference," Biometrika, Biometrika Trust, vol. 103(4), pages 829-842.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:4:p:829-842.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw047
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    2. Halloran M. Elizabeth & Hudgens Michael G., 2012. "Causal Inference for Vaccine Effects on Infectiousness," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-40, January.
    3. VanderWeele, Tyler J. & Tchetgen Tchetgen, Eric J., 2011. "Effect partitioning under interference in two-stage randomized vaccine trials," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 861-869, July.
    4. Xi Luo & Dylan S. Small & Chiang-Shan R. Li & Paul R. Rosenbaum, 2012. "Inference With Interference Between Units in an fMRI Experiment of Motor Inhibition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 530-541, June.
    5. Carolina Perez-Heydrich & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2014. "Assessing effects of cholera vaccination in the presence of interference," Biometrics, The International Biometric Society, vol. 70(3), pages 731-741, September.
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    Cited by:

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    2. Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2022.
    3. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03455978, HAL.
    4. Georgia Papadogeorgou & Kosuke Imai & Jason Lyall & Fan Li, 2022. "Causal inference with spatio‐temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1969-1999, November.
    5. Shaina J. Alexandria & Michael G. Hudgens & Allison E. Aiello, 2023. "Assessing intervention effects in a randomized trial within a social network," Biometrics, The International Biometric Society, vol. 79(2), pages 1409-1419, June.
    6. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    7. Sujatro Chakladar & Samuel Rosin & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2022. "Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring," Biometrics, The International Biometric Society, vol. 78(2), pages 777-788, June.
    8. Anish Agarwal & Sarah H. Cen & Devavrat Shah & Christina Lee Yu, 2022. "Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference," Papers 2210.11355, arXiv.org, revised Oct 2023.
    9. Elizabeth L. Ogburn & Ilya Shpitser & Youjin Lee, 2020. "Causal inference, social networks and chain graphs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1659-1676, October.
    10. Das, Tirthatanmoy & Polachek, Solomon, 2022. "The Econometrics of Antidotal Variables," IZA Discussion Papers 15558, Institute of Labor Economics (IZA).
    11. Lan Liu & Eric Tchetgen Tchetgen, 2022. "Regression‐based negative control of homophily in dyadic peer effect analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 668-678, June.
    12. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Mar 2024.

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