MALTS: Matching After Learning to Stretch

Abstract

We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to poor quality matches, particularly when there are irrelevant covariates. In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches. The learned distance metric stretches the covariate space according to each covariate's contribution to outcome prediction: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.

40 pages, 5 Tables, 12 Figures

References in corpus (3)

• Matching Methods for Causal Inference: A Review and a Look Forward
• A Framework for Optimal Matching for Causal Inference
• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

Cited by in corpus (5)

• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
• Causal Relational Learning
• Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
• Interpretable Almost Matching Exactly for Causal Inference
• Clinically Relevant Mediation Analysis using Controlled Indirect Effect