Year: 2025
Type: article
Abstract: Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariates, thereby reducing confounding and ensuring an unbiased estimation of treatment effects. We introduced a novel two-stage interpretable matching (TIM) framework for transparent and interpretable covariate matching. In the first stage, we perform exact matching across all available covariates. For treatment and control units without an exact match in the first stage, we proceed to the second stage. Here, we iteratively refine the matching process by removing the least significant confounder and attempting exact matching on the remaining covariates. We learn a distance metric for the dropped covariates to quantify closeness to the treatment unit(s) within the corresponding strata. We used these matches to estimate the conditional average treatment effects (CATEs). We conducted experiments on synthetic datasets with varying association structures and correlations. We assessed its performance by measuring bias in CATE estimation and evaluating multivariate overlap between treatment and control groups. Additionally, we apply TIM to a real-world healthcare dataset to estimate the causal effect of family history of obesity on obesity. Our results demonstrate that TIM improves CATE estimates, increases multivariate overlap, and scales effectively to large-scale data.
Authors: Sahil Shikalgar, Md. Noor-E-Alam
Institution: Northeastern University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4417462125