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

• URL: http://arxiv.org/abs/2505.20130v2
• Date: Sat, 21 Jun 2025 02:42:50 GMT
• Title: Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
• Authors: Jin Zhu, Jingyi Li, Hongyi Zhou, Yinan Lin, Zhenhua Lin, Chengchun Shi,
• Abstract summary: This paper focuses on the design of spatial experiments to optimize the amount of information derived from the experimental data.<n>We propose a surrogate function for the mean squared error (MSE) of the causal effect estimator.
• Score: 15.638574293473104
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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