ArticleBiometrics2019
Causal inference with interfering units for cluster and population level treatment allocation programs.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- Joint mixed-effects models for causal inference in clustered network-based observational studies.Statistical methods in medical research · 2026Article
- Bipartite interference and air pollution transport: estimating health effects of power plant interventions.Biostatistics (Oxford, England) · 2025Article
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- 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
- ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.Biometrika · 2023Article
- Generalized propensity score approach to causal inference with spatial interference.Biometrics · 2023Article
- Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.Journal of agricultural, biological, and environmental statistics · 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
- A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PMThe annals of applied statistics · 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
- Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring.Biometrics · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination.American journal of epidemiology · 2021Article
- Bipartite Causal Inference with Interference.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.Journal of the American Statistical Association · 2020Article
- CAUSAL INFERENCE FROM OBSERVATIONAL STUDIES WITH CLUSTERED INTERFERENCE, WITH APPLICATION TO A CHOLERA VACCINE STUDY.The annals of applied statistics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Interference arises when an individual's potential outcome depends on the individual treatment level, but also on the treatment level of others. A common assumption in the causal inference literature in the presence of interference is partial interference, implying that the population can be partitioned in clusters of individuals whose potential outcomes only depend on the treatment of units within the same cluster. Previous literature has defined average potential outcomes under counterfactual scenarios where treatments are randomly allocated to units within a cluster. However, within clusters there may be units that are more or less likely to receive treatment based on covariates or neighbors' treatment. We define new estimands that describe average potential outcomes for realistic counterfactual treatment allocation programs, extending existing estimands to take into consideration the units' covariates and dependence between units' treatment assignment. We further propose entirely new estimands for population-level interventions over the collection of clusters, which correspond in the motivating setting to regulations at the federal (vs. cluster or regional) level. We discuss these estimands, propose unbiased estimators and derive asymptotic results as the number of clusters grows. For a small number of observed clusters, a bootstrap approach for confidence intervals is proposed. Finally, we estimate effects in a comparative effectiveness study of power plant emission reduction technologies on ambient ozone pollution.
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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.