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Interpretable Almost-Exact Matching for Causal Inference.

Publication ,  Journal Article
Dieng, A; Liu, Y; Roy, S; Rudin, C; Volfovsky, A
Published in: Proceedings of machine learning research
April 2019

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.

Duke Scholars

Published In

Proceedings of machine learning research

EISSN

2640-3498

ISSN

2640-3498

Publication Date

April 2019

Volume

89

Start / End Page

2445 / 2453
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Dieng, A., Liu, Y., Roy, S., Rudin, C., & Volfovsky, A. (2019). Interpretable Almost-Exact Matching for Causal Inference. Proceedings of Machine Learning Research, 89, 2445–2453.
Dieng, Awa, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky. “Interpretable Almost-Exact Matching for Causal Inference.Proceedings of Machine Learning Research 89 (April 2019): 2445–53.
Dieng A, Liu Y, Roy S, Rudin C, Volfovsky A. Interpretable Almost-Exact Matching for Causal Inference. Proceedings of machine learning research. 2019 Apr;89:2445–53.
Dieng, Awa, et al. “Interpretable Almost-Exact Matching for Causal Inference.Proceedings of Machine Learning Research, vol. 89, Apr. 2019, pp. 2445–53.
Dieng A, Liu Y, Roy S, Rudin C, Volfovsky A. Interpretable Almost-Exact Matching for Causal Inference. Proceedings of machine learning research. 2019 Apr;89:2445–2453.

Published In

Proceedings of machine learning research

EISSN

2640-3498

ISSN

2640-3498

Publication Date

April 2019

Volume

89

Start / End Page

2445 / 2453