ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2014
Interference and Sensitivity Analysis.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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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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
28 citing papers in PubMed.
- Sensitivity analysis for contamination in egocentric-network randomized trials with interference.Biostatistics (Oxford, England) · 2026Article
- Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.Observational studies · 2026Article
- Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Renal function and risk of dementia: a Mendelian randomization study.Renal failure · 2024Article
- Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2024Article
- Dark loops: contagion effects, consistency and chemosocial matrices in psychedelic-assisted therapy trials.Psychological medicine · 2023Review
- ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.Biometrika · 2023Article
- Estimating Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs.The annals of applied statistics · 2023Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Letter to the editor regarding "Covid-19 transmission in fitness centers in Norway - a randomized trial".BMC public health · 2022Article
- 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
- Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.Clinical trials (London, England) · 2022Article
- Tutorial on directed acyclic graphs.Journal of clinical epidemiology · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- Estimating the Effectiveness of Rotavirus Vaccine Schedules.Epidemiology (Cambridge, Mass.) · 2021Article
- Auto-G-Computation of Causal Effects on a Network.Journal of the American Statistical Association · 2021Article
- Identification of causal intervention effects under contagion.Journal of causal inference · 2021Article
- Formulating causal questions and principled statistical answers.Statistics in medicine · 2020Article
- Examining the causal mediating role of brain pathology on the relationship between diabetes and cognitive impairment: the Cardiovascular Health Study.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020Article
- The Relative Economy and Drug Overdose Deaths.Epidemiology (Cambridge, Mass.) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Causal inference with interference is a rapidly growing area. The literature has begun to relax the "no-interference" assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
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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.