ArticleJournal of the American Statistical Association2014
Large sample randomization inference of causal effects in the presence of interference.
Article in Journal of the American Statistical Association, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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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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Who cites it
29 citing papers in PubMed.
- Design of egocentric network-based studies to estimate causal effects under interference.Statistical methods in medical research · 2025Article
- CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE.The annals of applied statistics · 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
- Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs.The annals of applied statistics · 2023Article
- Generalized propensity score approach to causal inference with spatial interference.Biometrics · 2023Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Estimating the total treatment effect in randomized experiments with unknown network structure.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Toward evaluation of disseminated effects of medications for opioid use disorder within provider-based clusters using routinely-collected health data.Statistics in medicine · 2022Article
- Article
- Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022Article
- AVERAGE TREATMENT EFFECTS IN THE PRESENCE OF UNKNOWN INTERFERENCE.Annals of statistics · 2021Article
- Bipartite Causal Inference with Interference.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Auto-G-Computation of Causal Effects on a Network.Journal of the American Statistical Association · 2021Article
- Causal inference, social networks and chain graphs.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020Article
- Causal inference with interfering units for cluster and population level treatment allocation programs.Biometrics · 2019Article
- A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance.Biometrics · 2019Article
- Causal inference when counterfactuals depend on the proportion of all subjects exposed.Biometrics · 2019Article
- Modelling and estimation for optimal treatment decision with interference.Stat (International Statistical Institute) · 2019Article
- Doubly Robust Estimation in Observational Studies with Partial Interference.Stat (International Statistical Institute) · 2019Article
- Assessing Individual and Disseminated Effects in Network-Randomized Studies.American journal of epidemiology · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recently, increasing attention has focused on making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this paper, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies and in the second stage individuals are randomized to treatment or control conditional on the strategy assigned to their group in the first stage. For this design, the asymptotic distributions of estimators of the causal effects are derived when either the number of individuals per group or the number of groups grows large. Under certain homogeneity assumptions, the asymptotic distributions provide justification for Wald-type confidence intervals (CIs) and tests. Empirical results demonstrate the Wald CIs have good coverage in finite samples and are narrower than CIs based on either the Chebyshev or Hoeffding inequalities provided the number of groups is not too small. The methods are illustrated by two examples which consider the effects of cholera vaccination and an intervention to encourage voting.
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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.