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Activity Number: 562 - Advances in Nonparametric Methods in Causal Inference
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #304859
Title: Model-Free Policy Evaluation
Author(s): Rina Friedberg* and Stefan Wager and Susan Athey
Companies: Stanford University and Stanford University and Stanford University
Keywords: Causal inference; Model-free; Policy learning

Machine learning for heterogeneous treatment effects has become a fast-growing research area in recent years. A popular and useful sub-field is policy learning, wherein we can use treatment effect estimates to make optimal intervention assignments. As researchers develop advanced nonparametric causal inference algorithms, there is a need for model-free tools to allow policy-makers to make principled, actionable decisions based on algorithm output. We introduce a set of methods that will allow practitioners to make valid statistical inferences in this setting, without imposing model restrictions. We propose specific hypotheses that correspond to real questions of interest about comparing policies, and give corresponding test statistics and asymptotic distributions, drawing from core statistical theory such as McNemar’s test. Additionally, we discuss visual diagnostics and global tests for any set of costs for which a personalized policy improves over a random or uniform policy, and demonstrate performance in a simulation study.

Authors who are presenting talks have a * after their name.

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