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MALTS: Matching After Learning to Stretch

Publication ,  Journal Article
Parikh, H; Rudin, C; Volfovsky, A
Published in: Journal.of.Machine.Learning.Research 23(240) (2022) 1-42
November 18, 2018

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.

Duke Scholars

Published In

Journal.of.Machine.Learning.Research 23(240) (2022) 1-42

Publication Date

November 18, 2018
 

Citation

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Parikh, H., Rudin, C., & Volfovsky, A. (2018). MALTS: Matching After Learning to Stretch. Journal.of.Machine.Learning.Research 23(240) (2022) 1-42.
Parikh, Harsh, Cynthia Rudin, and Alexander Volfovsky. “MALTS: Matching After Learning to Stretch.” Journal.of.Machine.Learning.Research 23(240) (2022) 1-42, November 18, 2018.
Parikh H, Rudin C, Volfovsky A. MALTS: Matching After Learning to Stretch. Journal.ofMachineLearningResearch 23(240) (2022) 1-42. 2018 Nov 18;
Parikh, Harsh, et al. “MALTS: Matching After Learning to Stretch.” Journal.of.Machine.Learning.Research 23(240) (2022) 1-42, Nov. 2018.
Parikh H, Rudin C, Volfovsky A. MALTS: Matching After Learning to Stretch. Journal.ofMachineLearningResearch 23(240) (2022) 1-42. 2018 Nov 18;

Published In

Journal.of.Machine.Learning.Research 23(240) (2022) 1-42

Publication Date

November 18, 2018