ArticleStat (International Statistical Institute)2019
Doubly Robust Estimation in Observational Studies with Partial Interference.
Article in Stat (International Statistical Institute), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Joint mixed-effects models for causal inference in clustered network-based observational studies.Statistical methods in medical research · 2026Article
- G-estimation with partial interference.Stat (International Statistical Institute) · 2025Article
- Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference.Journal of the American Statistical Association · 2025Article
- G-formula for observational studies under stratified interference, with application to bed net use on malaria.Statistics in medicine · 2024Article
- Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Recent Developments in Causal Inference and Machine Learning.Annual review of sociology · 2023Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Propensity Score in the Face of Interference: Discussion of.Observational studies · 2023Article
- Targeted maximum likelihood estimation of causal effects with interference: A simulation study.Statistics in medicine · 2022Article
- Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring.Biometrics · 2022Article
- Effect pathways of informal family separation on children's outcomes: Paternal labor migration and long-term educational attainment of left-behind children in rural China.Social science research · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.