Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
Abstract
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.
Cite
Text
Zhu et al. "Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Zhu et al. "Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/zhu2025icml-balancing/)BibTeX
@inproceedings{zhu2025icml-balancing,
title = {{Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach}},
author = {Zhu, Jin and Li, Jingyi and Zhou, Hongyi and Lin, Yinan and Lin, Zhenhua and Shi, Chengchun},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {79918-79937},
volume = {267},
url = {https://mlanthology.org/icml/2025/zhu2025icml-balancing/}
}