Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
This work addresses the challenge of confounding and selection bias in causal inference for observational studies, offering a method to improve treatment effect estimation in domains like public health, though it is incremental as it builds on existing random forest techniques.
The paper tackled the problem of estimating individual treatment effects in observational data by using random forest methods within a counterfactual framework, finding that adaptive methods like counterfactual synthetic forests achieve accurate estimation in complex settings.
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.