用图切割法优化空间实验设计，提升因果效应估计精度。
Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
- 基于均方误差代理函数，结合图切割算法找最优实验布局。
- 在模拟和城市网约车调度场景中，显著降低估计误差。
- 适用于有空间干扰的大型实验，计算高效且可扩展。
本文聚焦于空间实验设计，旨在优化实验数据中的信息量并提高因果效应估计的准确性。我们提出了一种均方误差（MSE）估计器的代理函数，使经典图切割算法可用于学习最优实验设计。该方法具有三大优势：(1) 能处理中到大程度的空间干扰；(2) 适应不同空间协方差函数；(3) 计算高效。理论分析与基于合成环境及城市级网约车调度仿真器的数值实验进一步验证了其有效性。代码已开源：https://github.com/Mamba413/CausalGraphCut。
原文摘要 · Abstract (English)
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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