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February 2019 Comment: Causal Inference Competitions: Where Should We Aim?
Ehud Karavani, Tal El-Hay, Yishai Shimoni, Chen Yanover
Statist. Sci. 34(1): 86-89 (February 2019). DOI: 10.1214/18-STS679


Data competitions proved to be highly beneficial to the field of machine learning, and thus expected to provide similar advantages in the field of causal inference. As participants in the 2016 and 2017 Atlantic Causal Inference Conference (ACIC) data competitions and co-organizers of the 2018 competition, we discuss the strengths of simulation-based competitions and suggest potential extensions to address their limitations. These suggested augmentations aim at making the data generating processes more realistic and gradually increase in complexity, allowing thorough investigations of algorithms’ performance. We further outline a community-wide competition framework to evaluate an end-to-end causal inference pipeline, beginning with a causal question and a database, and ending with causal estimates.


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Ehud Karavani. Tal El-Hay. Yishai Shimoni. Chen Yanover. "Comment: Causal Inference Competitions: Where Should We Aim?." Statist. Sci. 34 (1) 86 - 89, February 2019.


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

zbMATH: 07110679
MathSciNet: MR3938968
Digital Object Identifier: 10.1214/18-STS679

Keywords: automated algorithms , Causal inference , competition , data challenge , evaluation , machine learning

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.34 • No. 1 • February 2019
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