Open Access
February 2019 Comment: Strengthening Empirical Evaluation of Causal Inference Methods
David Jensen
Statist. Sci. 34(1): 77-81 (February 2019). DOI: 10.1214/18-STS690


This is a contribution to the discussion of the paper by Dorie et al. (Statist. Sci. 34 (2019) 43–68), which reports the lessons learned from 2016 Atlantic Causal Inference Conference Competition. My comments strongly support the authors’ focus on empirical evaluation, using examples and experience from machine learning research, particularly focusing on the problem of algorithmic complexity. I argue that even broader and deeper empirical evaluation should be undertaken by the researchers who study causal inference. Finally, I highlight a few key conclusions that suggest where future research should focus.


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David Jensen. "Comment: Strengthening Empirical Evaluation of Causal Inference Methods." Statist. Sci. 34 (1) 77 - 81, February 2019.


Published: February 2019
First available in Project Euclid: 12 April 2019

zbMATH: 07110677
MathSciNet: MR3938966
Digital Object Identifier: 10.1214/18-STS690

Keywords: algorithmic complexity , alignment , Causal inference , constructed observational studies , empirical evaluation , machine learning

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.34 • No. 1 • February 2019
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