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

TL;DR

This paper introduces a graph cut-based method for designing spatial experiments that balances interference and correlation, improving causal effect estimation in complex, large-scale environments.

Contribution

It presents a novel surrogate function for MSE that enables efficient, scalable spatial experimental design accommodating interference and diverse covariance structures.

Findings

Effective in moderate to large interference settings

Adapts to various spatial covariance functions

Validated through synthetic and real-world simulations

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.