High-Dimensional Matching with Genetic Algorithms

Music City Center 

Matching in observational studies estimates causal effects by balancing covariate distributions between treated and control groups. Traditional methods rely on pairwise distances, but in high-dimensional, low-sample size settings, the curse of dimensionality makes it difficult to distinguish observations. To address this, we propose a novel matching method using genetic algorithms, shifting focus from individual- to group-level distances. Our method improves causal effect estimation by optimizing the similarity of high-dimensional joint covariate distributions. This approach has key advantages: (1) it avoids dimension reduction, preserving full covariate information without additional modeling; (2) it maintains transparency by not relying on outcomes, akin to traditional matching; and (3) it is robust in low-sample size settings, where traditional methods may struggle. Moreover, our results show the proposed method is competitive with existing approaches even in low-dimensional cases. Through simulations and real data applications, we validate its performance, offer practical guidance, and highlight its potential as a tool for causal inference in high- and low-dimensional settings.

Matching

High-dimensional data

Genetic algorithms

Covariate balance

Low-sample size settings

Causal inference 

Korean International Statistical Society