Abstract：
Existing methods that use propensity scores for heterogeneous treatment effect estimation on non-experimental data do not readily extend to the case of more than two treatment options. In this work, we develop a new propensity score-based method for heterogeneous treatment effect estimation when there are three or more treatment options, and prove that it generates unbiased estimates. We demonstrate our method on a real patient registry of patients in Singapore with diabetic dyslipidemia. On this dataset, our method generates heterogeneous treatment recommendations for patients among three options: Statins, fibrates, and non-pharmacological treatment to control patients\' lipid ratios (total cholesterol divided by high-density lipoprotein level). In our numerical study, our proposed method generated more stable estimates compared to a benchmark method based on a multi-dimensional propensity score.
摘要：
使用倾向评分对非实验数据进行异质治疗效果评估的现有方法不容易扩展到超过两种治疗方案的情况。在这项工作中,当存在三种或更多种治疗方案时，我们开发了一种新的基于倾向评分的方法来估计异质治疗效果，并证明它产生无偏估计。我们在新加坡糖尿病血脂异常患者的真实患者注册表上展示了我们的方法。在这个数据集上，我们的方法为患者提供了三种不同的治疗建议：他汀类药物，贝多类,和非药物治疗以控制患者的血脂比率（总胆固醇除以高密度脂蛋白水平）。在我们的数值研究中，与基于多维倾向得分的基准方法相比,我们提出的方法产生了更稳定的估计值.
