Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods.

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 that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach 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 b

SUBMITTER: Lu M 

PROVIDER: S-EPMC5920646 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature