ArticleUncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence2019
Causal Inference Under Interference And Network Uncertainty.
Article in Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 16 citations in OpenAlex.
- Discussion on 'Causal inference with misspecified network interference structure' by Bar Weinstein and Daniel Nevo.Biometrics · 2026Article
- Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification.Biostatistics (Oxford, England) · 2025Article
- Applying causality to environmental security in Iraq.Scientific reports · 2025Article
- 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
- Causal Inference in the Presence of Interference in Sponsored Search Advertising.Frontiers in big data · 2022Article
- Identification of causal intervention effects under contagion.Journal of causal inference · 2021Article
- Causal inference, social networks and chain graphs.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 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 at 1 institution in 1 country.
Funding
Abstract
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly [10, 36, 20], but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.
Identifiers
31885520PMC6935347W2953584959What 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.