“Automated Versus Do-It-Yourself Methods of Causal Inference: Lessons Learned from a Data Analysis Competition,” Jennifer Hill, NYU

Event time: 
Thursday, April 26, 2018 - 12:00pm to 1:15pm
Institution for Social and Policy Studies (PROS077 ), A002 See map
77 Prospect Street
New Haven, CT 06511
Event description: 


Abstract: Statisticians have made great strides towards assumption-free estimation of causal estimands in the past few decades. 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. R tools available to help researchers take advantage of the simulations and resulting datasets will be described.

Jennifer Hill is Professor of Applied Statistics and Data Science at NYU where she co-directs the PRIISM Applied Statistics Center and an Applied Statistics MS program. Her work focuses on causal inference, missing data, multilevel models, and Bayesian nonparametric modeling for the social, behavioral and education sciences.

This workshop series is being sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.

Open to: 
General Public