  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 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 drug use plays in sexual risk. The analysis reveals
important connections between risky behavior, drug usage, and sexual risk.
