Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

Min Lu, Saad Sadiq, Daniel J Feaster, Hemant Ishwaran

Research output: Contribution to journalArticle

17 Scopus citations

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 be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk. Supplementary material for this article is available online.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - Jan 30 2018

Keywords

  • Counterfactual model
  • Individual treatment effect (ITE)
  • Propensity score
  • Synthetic forests
  • Treatment heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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