Random forests for individual treatment effect estimation with the R package ITERF

Treatment effects often vary across individuals within a population. In contexts such as personalized medicine, it is crucial to accurately estimate treatment effects at the individual level. Random forests are among the most popular, versatile, and efficient statistical learning methods. This article introduces the R package ITERF, designed to estimate individual treatment effects using random forests across various settings. In particular, new methods to estimate the maximum treatment effect are introduced.

The ITERF package provides methods for estimating treatment effects in two scenarios: (1) survival outcomes with right-censoring and a binary treatment, and (2) continuous outcomes with a continuous treatment. All methods are based on random forests. A simulation study demonstrates that the proposed methods for estimating the maximum treatment effect perform as expected and show considerable promise. An illustration, using real data, that explores the link between sleep duration and cognitive health in the elderly is given.

The ITERF package offers a fast and user-friendly tool for estimating treatment effect measures using random forests, making it a valuable resource for researchers and practitioners in personalized treatment evaluation.