ArticleThe annals of applied statistics2025
HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.
Article in The annals of applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.Current environmental health reports · 2026Review
- HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.The annals of applied statistics · 2025Article
- Two-step pragmatic subgroup discovery for heterogeneous treatment effects analyses: perspectives toward enhanced interpretability.European journal of epidemiology · 2025Article
- CRE: An R package for interpretable discovery and inference of heterogeneous treatment effects.Journal of open source software · 2023Article
- 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 control by a constrained external agent as a continuous optimization problem.Scientific reports · 2022Article
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.
Funding
Abstract
The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and interference within clusters. The proposed network causal tree (NCT) algorithm has several advantages. First, it allows the investigation of the heterogeneity of the treatment effect, avoiding potential bias due to the presence of interference. Second, understanding the heterogeneity of both treatment and spillover effects can guide policymakers in scaling up interventions, designing targeting strategies, and increasing cost-effectiveness. We investigate the performance of our NCT method using a Monte Carlo simulation study and illustrate its application to assess the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.
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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.