mlsanityData to 14 Oct 2026

Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach

arXiv 2505.20130 ↗First posted 26 May 2025cs.LG stat.CO stat.MLcited by 5

Not on the map. Why?

Abstract (arXiv)

This paper focuses on the design of spatial experiments to optimize the amount of information derived from the experimental data and enhance the accuracy of the resulting causal effect estimator. We propose a surrogate function for the mean squared error (MSE) of the estimator, which facilitates the use of classical graph cut algorithms to learn the optimal design. Our proposal offers three key advances: (1) it accommodates moderate to large spatial interference effects; (2) it adapts to different spatial covariance functions; (3) it is computationally efficient. Theoretical results and numerical experiments based on synthetic environments and a dispatch simulator that models a city-scale ridesharing market, further validate the effectiveness of our design. A python implementation of our method is available at https://github.com/Mamba413/CausalGraphCut.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 71 of its 130 references that are in the map. First posted on arXiv 26 May 2025.

Builds on

71 of 130 references.

References · 71

  1. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 339
  2. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 286
  3. 2019Batch Policy Learning under Constraintscited by 244
  4. 2018Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimationcited by 227
  5. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 106
  6. 2019Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learningcited by 75
  7. 2020Batch Policy Learning in Average Reward Markov Decision Processescited by 69
  8. 2016Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningcited by 65
  9. 2020Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settingscited by 65
  10. 2020CoinDICE: Off-Policy Confidence Interval Estimationcited by 64
  11. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 46
  12. 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
38 more
  1. 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 44
  2. 2013Graph cluster randomization: network exposure to multiple universescited by 42
  3. 2021Bootstrapping Fitted Q-Evaluation for Off-Policy Inferencecited by 40
  4. 2020Causal Inference under Networked Interference and Intervention Policy Enhancementcited by 38
  5. 2020Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weightingcited by 38
  6. 2020Design and Analysis of Switchback Experimentscited by 38
  7. 2020Accountable Off-Policy Evaluation With Kernel Bellman Statisticscited by 36
  8. 2021Deeply-Debiased Off-Policy Interval Estimationcited by 36
  9. 2018De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representerscited by 34
  10. 2022On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluationcited by 33
  11. 2022Off-Policy Confidence Interval Estimation with Confounded Markov Decision Processcited by 31
  12. 2020Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Frameworkcited by 29
  13. 2019Causal Inference Under Interference And Network Uncertaintycited by 27
  14. 2017Data-Efficient Policy Evaluation Through Behavior Policy Searchcited by 26
  15. 2021Off-Policy Evaluation in Partially Observed Markov Decision Processes under Sequential Ignorabilitycited by 24
  16. 2021Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platformscited by 22
  17. 2022Markovian Interference in Experimentscited by 22
  18. 2021Multiple Randomization Designs: Estimation and Inference with Interferencecited by 20
  19. 2022An Instrumental Variable Approach to Confounded Off-Policy Evaluationcited by 20
  20. 2022Switchback Experiments under Geometric Mixingcited by 17
  21. 2019Experimenting in Equilibriumcited by 16
  22. 2021Projected State-action Balancing Weights for Offline Reinforcement Learningcited by 16
  23. 2021Treatment Effects in Market Equilibriumcited by 16
  24. 2018Bipartite Causal Inference with Interferencecited by 15
  25. 2021Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learningcited by 15
  26. 2022Safe Exploration for Efficient Policy Evaluation and Comparisoncited by 15
  27. 2021Rate-Optimal Cluster-Randomized Designs for Spatial Interferencecited by 14
  28. 2023Efficient Policy Evaluation with Offline Data Informed Behavior Policy Designcited by 14
  29. 2019Balancing Covariates in Randomized Experiments with the Gram-Schmidt Walk Designcited by 13
  30. 2020A review of spatial causal inference methods for environmental and epidemiological applicationscited by 12
  31. 2024Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choicescited by 12
  32. 2024Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designscited by 12
  33. 2021Average Direct and Indirect Causal Effects under Interferencecited by 10
  34. 2023Causal clustering: design of cluster experiments under network interferencecited by 10
  35. 2024Multi-Armed Bandits with Interferencecited by 10
  36. 2024Online Experimental Design With Estimation-Regret Trade-off Under Network Interferencecited by 9
  37. 2020Minimizing Interference and Selection Bias in Network Experiment Designcited by 8
  38. 2020Network experimentation at scalecited by 8

50 most cited of 71 All 71 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Doubly Robust Alignment for Large Language Modelscited by 12
  2. 2025A Two-armed Bandit Framework for A/B Testingcited by 2
  3. 2026Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learningcited by 1
  4. 2026Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensionscited by 0
  5. 2026Robust Sequential Experimental Design for A/B Testingcited by 0