ArticleJournal of the American Statistical Association2024
Causal Inference for Social Network Data.
Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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.
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.
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Who cites it
33 citing papers in PubMed.
- Ten Healthcare Delivery Trends and Their Measurement and Methodological Implications for Cancer Health Services Research.Health services research · 2026Article
- Doubly Robust Causal Estimation Under Multi-View Network Interference (Student Abstract).Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Sensitivity analysis for contamination in egocentric-network randomized trials with interference.Biostatistics (Oxford, England) · 2026Article
- Discussion on 'Causal inference with misspecified network interference structure' by Bar Weinstein and Daniel Nevo.Biometrics · 2026Article
- HK-DeepIV: heat-kernel geometric diagnostics and early-warning signals for curvature-induced interference in financial correlation networks.Frontiers in big data · 2026Article
- Quantifying excess TB prevalence among people living with HIV on ART in the universal ART era: Evidence from a TB prevalence survey in Zambia and South Africa.PLOS global public health · 2026Article
- Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.Observational studies · 2026Article
- Bipartite interference and air pollution transport: estimating health effects of power plant interventions.Biostatistics (Oxford, England) · 2025Article
- Design of egocentric network-based studies to estimate causal effects under interference.Statistical methods in medical research · 2025Article
- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
- Article
- A methodological approach for inferring causal relationships from opinions and news-derived events with an application to climate change.PeerJ. Computer science · 2025Article
- Causal inference over stochastic networks.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 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
- 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
- Article
- Recent Developments in Causal Inference and Machine Learning.Annual review of sociology · 2023Article
- Article
- Taking the problem of colliders seriously in the study of crime: A research note.Journal of experimental criminology · 2023Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties and for dependence due to latent similarities among nodes sharing ties. We propose new causal effects that are specifically of interest in social network settings, such as interventions on network ties and network structure. We use our methods to reanalyze an influential and controversial study that estimated causal peer effects of obesity using social network data from the Framingham Heart Study; after accounting for network structure we find no evidence for causal peer effects.
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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.