Interpretable Almost-Exact Matching for Causal Inference.

Journal Article (Journal Article)

Matching methods are heavily used in the social and health sciences due to their interpretability. We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. The method proposed in this work aims to match units on a weighted Hamming distance, taking into account the relative importance of the covariates; the algorithm aims to match units on as many relevant variables as possible. To do this, the algorithm creates a hierarchy of covariate combinations on which to match (similar to downward closure), in the process solving an optimization problem for each unit in order to construct the optimal matches. The algorithm uses a single dynamic program to solve all of the units' optimization problems simultaneously. Notable advantages of our method over existing matching procedures are its high-quality interpretable matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.

Full Text

Duke Authors

Cited Authors

  • Dieng, A; Liu, Y; Roy, S; Rudin, C; Volfovsky, A

Published Date

  • April 2019

Published In

  • Proceedings of Machine Learning Research

Volume / Issue

  • 89 /

Start / End Page

  • 2445 - 2453

PubMed ID

  • 31198908

Pubmed Central ID

  • PMC6563929

Electronic International Standard Serial Number (EISSN)

  • 2640-3498

International Standard Serial Number (ISSN)

  • 2640-3498


  • eng