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Stat (Int Stat Inst). 2019;8(1). pii: e214. doi: 10.1002/sta4.214. Epub 2019 Jan 10.

Doubly Robust Estimation in Observational Studies with Partial Interference.

Author information

1
School of Statistics, University of Minnesota at Twin Cities, Minnsota, U.S.A.
2
Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, U.S.A.
3
Department of Epidemiology, University of California, Los Angeles, California, U.S.A.
4
Department of International Health, Johns Hopkins University, Maryland, U.S.A.
5
Department of Geography, University of North Carolina at Chapel Hill, North Carolina, U.S.A.

Abstract

Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings it may be reasonable to assume individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, i.e., there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been proposed of different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modeled. Alternatively, one can estimate the treatment effect using an outcome regression model. In this paper, we propose doubly robust (DR) estimators which utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified. Empirical results are presented to demonstrate the DR property of the proposed estimators, as well as the efficiency gain of DR over IPW estimators when both models are correctly specified. The different estimators are illustrated using data from a study examining the effects of cholera vaccination in Bangladesh.

KEYWORDS:

Causal Inference; Doubly Robust Estimator; Interference; Observational Studies

PMID:
31440374
PMCID:
PMC6706246
[Available on 2020-01-10]
DOI:
10.1002/sta4.214

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