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Activity Number: 446 - Prediction vs. Inference in Personalized Medicine
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Mental Health Statistics Section
Abstract #322087
Title: Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
Author(s): Hemant Ishwaran*
Companies: University of Miami
Keywords: Counterfactual Model ; Individual Treatment Effect ; Synthetic forests ; Treatment Heterogeneity
Abstract:

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find accurate estimation of individual treatment effects is possible even in complex heterogeneous settings but that the type of RF approach used plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, in order to explore the role race plays in sexual risk. The analysis reveals an important connection between risky behavior, race, and sexual risk.


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

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