  Individualized treatment effect lies at the heart of precision medicine.
Interpretable individualized treatment rules (ITRs) are desirable for
clinicians or policymakers due to their intuitive appeal and transparency. The
gold-standard approach to estimating the ITRs is randomized experiments, where
subjects are randomized to different treatment groups and the bias is minimized
to the extent possible. However, experimental data are limited in external
validity because of their selection restrictions and therefore are not
representative of the target real-world population. Conventional learning
methods of optimal interpretable ITRs for a target population based only on
experimental data are biased. On the other hand, real-world data (RWD) are
becoming popular and provide a representative sample of the population. To
learn the generalizable optimal interpretable ITRs, we propose an integrative
transfer learning method based on weighting schemes to calibrate the covariate
distribution of the experiment to that of the RWD. We show that the proposed
ITR estimator has a theoretical guarantee of the risk consistency. We evaluate
the transfer learner based on the finite-sample performance through simulation
and apply it to a real data application of a job training program.
