mlsanityData to 14 Oct 2026

Higher-Order Causal Message Passing for Experimentation with Complex Interference

arXiv 2411.00945 ↗First posted 1 Nov 2024cs.LG econ.EM stat.MLcited by 4

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Abstract (arXiv)

Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences and online marketplaces, where treating one experimental unit can influence outcomes for others through direct or indirect interactions. Such interference can lead to biased treatment effect estimates, particularly when the structure of these interactions is unknown. We address this challenge by introducing a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data to estimate the evolution of the system state. Concretely, we construct non-linear features from the moments of unit outcomes and treatments and then learn a function that maps these features to future mean and variance of unit outcomes. This allows for the estimation of the treatment effect over time. Extensive simulations across multiple domains, using synthetic and real network data, demonstrate the efficacy of our approach in estimating total treatment effect dynamics, even in cases where interference exhibits non-monotonic behavior in the probability of treatment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 29 of its 67 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

29 of 67 references.

References · 29

  1. 2021Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddingscited by 49
  2. 2014Design and analysis of experiments in networks: Reducing bias from interferencecited by 42
  3. 2020Experimental Design in Two-Sided Platforms: An Analysis of Biascited by 39
  4. 2019Causal Inference Under Interference And Network Uncertaintycited by 27
  5. 2022Markovian Interference in Experimentscited by 22
  6. 2022Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structurecited by 18
  7. 2019Experimenting in Equilibriumcited by 16
  8. 2021Treatment Effects in Market Equilibriumcited by 16
  9. 2020Randomized Graph Cluster Randomizationcited by 14
  10. 2019Optimal Experimental Design for Staggered Rolloutscited by 13
  11. 2020Reducing Interference Bias in Online Marketplace Pricing Experimentscited by 13
  12. 2017Elements of estimation theory for causal effects in the presence of network interferencecited by 12
17 more
  1. 2023Correcting for Interference in Experiments: A Case Study at Douyincited by 12
  2. 2023Causal clustering: design of cluster experiments under network interferencecited by 10
  3. 2020Experimental Design under Network Interferencecited by 9
  4. 2021Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Modelcited by 9
  5. 2022Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledgecited by 8
  6. 2022Neighborhood Adaptive Estimators for Causal Inference under Network Interferencecited by 8
  7. 2022Network Interference in Micro-Randomized Trialscited by 7
  8. 2023Causal Message Passing for Experiments with Unknown and General Network Interferencecited by 7
  9. 2017Limitations of design-based causal inference and A/B testing under arbitrary and network interferencecited by 6
  10. 2021The Local Approach to Causal Inference under Network Interferencecited by 6
  11. 2020Policy design in experiments with unknown interferencecited by 5
  12. 2022Reducing Marketplace Interference Bias Via Shadow Pricescited by 5
  13. 2023Modeling Interference Using Experiment Roll-outcited by 5
  14. 2018Central limit theorems via Stein's method for randomized experiments under interferencecited by 4
  15. 2018A systematic investigation of classical causal inference strategies under mis-specification due to network interferencecited by 4
  16. 2016Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effectscited by 3
  17. 2023Optimized Covariance Design for AB Test on Social Network under Interferencecited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Simulating and Experimenting with Social Media Mobilization Using LLM Agentscited by 3
  2. 2025On Evolution-Based Models for Experimentation Under Interferencecited by 1
  3. 2026Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interferencecited by 0
  4. 2026Causal Effects with Unobserved Unit Types in Interacting Human-AI Systemscited by 0