Statistical Science

Comment: Will Competition-Winning Methods for Causal Inference Also Succeed in Practice?

Qingyuan Zhao, Luke J. Keele, and Dylan S. Small

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Abstract

First, we would like to congratulate the authors for successfully hosting the causal inference data competition (referred to as Competition henceforth) and contributing a unique and thought-provoking article to the literature. The authors have provided a comprehensive and timely platform to evaluate the ever-growing number of methods used for covariate adjustment in observational studies. In our comment, we don’t generally question the results of the competition, but we do wish to emphasize several other key elements about the role statistics plays in causal inference and observational studies.

Article information

Source
Statist. Sci., Volume 34, Number 1 (2019), 72-76.

Dates
First available in Project Euclid: 12 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.ss/1555056032

Digital Object Identifier
doi:10.1214/18-STS680

Mathematical Reviews number (MathSciNet)
MR3938965

Keywords
Observational studies machine learning study design

Citation

Zhao, Qingyuan; Keele, Luke J.; Small, Dylan S. Comment: Will Competition-Winning Methods for Causal Inference Also Succeed in Practice?. Statist. Sci. 34 (2019), no. 1, 72--76. doi:10.1214/18-STS680. https://projecteuclid.org/euclid.ss/1555056032


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See also

  • Main article: Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition.