Open Access
February 2019 Comment on “Automated Versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition”
Susan Gruber, Mark J. van der Laan
Statist. Sci. 34(1): 82-85 (February 2019). DOI: 10.1214/18-STS689

Abstract

Dorie and co-authors (DHSSC) are to be congratulated for initiating the ACIC Data Challenge. Their project engaged the community and accelerated research by providing a level playing field for comparing the performance of a priori specified algorithms. DHSSC identified themes concerning characteristics of the DGP, properties of the estimators, and inference. We discuss these themes in the context of targeted learning.

Citation

Download Citation

Susan Gruber. Mark J. van der Laan. "Comment on “Automated Versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition”." Statist. Sci. 34 (1) 82 - 85, February 2019. https://doi.org/10.1214/18-STS689

Information

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

zbMATH: 07110678
MathSciNet: MR3938967
Digital Object Identifier: 10.1214/18-STS689

Keywords: Causal inference , Targeted learning , TMLE

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.34 • No. 1 • February 2019
Back to Top