  Assessing heterogeneous treatment effects has become a growing interest in
advancing precision medicine. Individualized treatment effects (ITE) play a
critical role in such an endeavor. Concerning experimental data collected from
randomized trials, we put forward a method, termed random forests of
interaction trees (RFIT), for estimating ITE on the basis of interaction trees
(Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate
(SSS) method, as an alternative to greedy search, to speed up tree
construction. RFIT outperforms the traditional `separate regression' approach
in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT
can be obtained with the infinitesimal jackknife method. We assess and
illustrate the use of RFIT via both simulation and the analysis of data from an
acupuncture headache trial.
