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.

翻译：个人化治疗效果是精密医学的核心。由于临床医生或决策者的直觉吸引力和透明度,对诊断性个人化治疗规则(ITRs)是可取的。黄金标准估算ITRs的方法是随机的实验,对不同治疗群体进行随机处理,尽可能缩小偏向。然而,实验数据在外部有效性方面是有限的,因为其选择限制,因此不代表目标的现实人口。仅仅根据实验数据对目标人口进行最佳解释性ITRs的常规学习方法是有偏向的。另一方面,真实世界数据(RWD)正在变得流行,并提供了具有代表性的人口样本。为了了解可普遍采用的最佳解释性ITRs,我们建议根据加权计划采用综合转移学习方法,以校准实验的共变分布与RWD的分布。我们表明,拟议的ITR估计者在理论上保证风险的一致性。我们根据通过模拟的有限模量性表现对转移学习者进行评估,并将它应用于职业培训方案的实际数据应用。