ArticleAnnals of statistics2021
AVERAGE TREATMENT EFFECTS IN THE PRESENCE OF UNKNOWN INTERFERENCE.
Article in Annals of statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Are arms-based methods required to test the stable unit treatment value assumption (SUTVA) among Cochrane reviews of contagious infection prevention interventions?Global epidemiology · 2026Article
- Article
- 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
- Design of egocentric network-based studies to estimate causal effects under interference.Statistical methods in medical research · 2025Article
- What randomization can and cannot guarantee.Observational studies · 2025Article
- Causal message-passing for experiments with unknown and general network interference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Causal inference over stochastic networks.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Multilevel Intervention Stepped Wedge Designs (MLI-SWDs).Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Instrumental variable analysis for cost outcome: Application to the effect of primary care visit on medical cost among low-income adults.Statistics in medicine · 2023Article
- Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Network experiment designs for inferring causal effects under interference.Frontiers in big data · 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
- Randomization for the susceptibility effect of an infectious disease intervention.Journal of mathematical biology · 2022Article
- A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study.BMC medical research methodology · 2022Article
- 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
- Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination.American journal of epidemiology · 2021Article
- Estimating the Effectiveness of Rotavirus Vaccine Schedules.Epidemiology (Cambridge, Mass.) · 2021Article
- Estimands and inference in cluster-randomized vaccine trials.Pharmaceutical statistics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and unknown interference. The rates of convergence depend on the rate at which the amount of interference grows and the degree to which it aligns with dependencies in treatment assignment. Importantly for practitioners, the results imply that if one erroneously assumes that units do not interfere in a setting with limited, or even moderate, interference, standard estimators are nevertheless likely to be close to an average treatment effect if the sample is sufficiently large. Conventional confidence statements may, however, not be accurate.
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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.