MALTS: Matching After Learning to Stretch
This work addresses the issue of irrelevant covariates in matching for causal inference, offering a flexible and interpretable solution that improves match quality.
The paper tackles the problem of poor quality matches in causal inference due to ad-hoc distance metrics by learning an interpretable distance metric that stretches covariate space based on importance, resulting in substantially higher quality matches for estimating conditional average treatment effects.
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.