Open Access
February 2019 Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition
Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone
Statist. Sci. 34(1): 43-68 (February 2019). DOI: 10.1214/18-STS667


Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However, this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend. Relatedly, researchers advocating for new methods typically compare their method to at best 2 or 3 other causal inference strategies and test using simulations that may or may not be designed to equally tease out flaws in all the competing methods. The causal inference data analysis challenge, “Is Your SATT Where It’s At?”, launched as part of the 2016 Atlantic Causal Inference Conference, sought to make progress with respect to both of these issues. The researchers creating the data testing grounds were distinct from the researchers submitting methods whose efficacy would be evaluated. Results from 30 competitors across the two versions of the competition (black-box algorithms and do-it-yourself analyses) are presented along with post-hoc analyses that reveal information about the characteristics of causal inference strategies and settings that affect performance. The most consistent conclusion was that methods that flexibly model the response surface perform better overall than methods that fail to do so. Finally new methods are proposed that combine features of several of the top-performing submitted methods.


Download Citation

Vincent Dorie. Jennifer Hill. Uri Shalit. Marc Scott. Dan Cervone. "Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition." Statist. Sci. 34 (1) 43 - 68, February 2019.


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

zbMATH: 07110674
MathSciNet: MR3938963
Digital Object Identifier: 10.1214/18-STS667

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

Rights: Copyright © 2019 Institute of Mathematical Statistics

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