ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2020
Causal inference, social networks and chain graphs.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Causal inference over stochastic networks.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 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
- A Method to Create Directed Acyclic Graphs from Cycles of Transmission of Zoonotic and Vector-Borne Infectious Agents.Vector borne and zoonotic diseases (Larchmont, N.Y.) · 2023Review
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Does deforestation drive visceral leishmaniasis transmission? A causal analysis.Proceedings. Biological sciences · 2021Article
- Identification of causal intervention effects under contagion.Journal of causal inference · 2021Article
- General Identification of Dynamic Treatment Regimes Under Interference.Proceedings of machine learning research · 2020Article
- Intervening on Network Ties.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate and justify a parsimonious parameterization for network data with interference and contagion. Our parameterization corresponds to a particular family of graphical models known as chain graphs. We argue that, in some settings, chain graph models approximate the marginal distribution of a snapshot of a longitudinal data-generating process on interacting units. We illustrate the use of chain graphs for causal inference about collective decision making in social networks by using data from US Supreme Court decisions between 1994 and 2004 and in simulations.
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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.