ArticleJournal of the American Statistical Association2021
Auto-G-Computation of Causal Effects on a Network.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 18 citations in OpenAlex.
- Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices.Social science & medicine (1982) · 2024Trial
- Sensitivity analysis for contamination in egocentric-network randomized trials with interference.Biostatistics (Oxford, England) · 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
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
- Causal inference over stochastic networks.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Article
- 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
- Causal Inference for Social Network Data.Journal of the American Statistical Association · 2024Article
- Finding influential subjects in a network using a causal framework.Biometrics · 2023Review
- Causal models and causal modelling in obesity: foundations, methods and evidence.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- 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
- The Effect of Family Wealth on Physical Function Among Older Adults in Mpumalanga, South Africa: A Causal Network Analysis.International journal of public health · 2023Article
- Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs.Science advances · 2022Article
- Targeted maximum likelihood estimation of causal effects with interference: A simulation study.Statistics in medicine · 2022Article
- Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring.Biometrics · 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
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
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 at 3 institutions in 1 country.
Funding
Abstract
Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the
Indexed as
Identifiers
What OpenQuestion holds
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.