Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatment effect estimation method in a multi-treatment setting. Our non-parametric modeling of the response curves relies on radial basis function (RBF)-nets with shared hidden neurons. Our model thus facilitates modeling commonality among the treatment outcomes. The estimation and inference schemes are developed under a Bayesian framework and implemented via an efficient Markov chain Monte Carlo algorithm, appropriately accommodating uncertainty in all aspects of the analysis. The numerical performance of the method is demonstrated through simulation experiments. Applying our proposed method to MIMIC data, we obtain several interesting findings related to the impact of different treatment strategies on the length of ICU stay and 12-hour SOFA score for sepsis patients who are home-discharged.

翻译：异质性治疗效果估计是精准医疗中的一个重要问题，其具体兴趣在于基于某些外部协变量识别不同治疗方案的差异化效应。我们提出了一种在多治疗方案背景下的新型非参数治疗效果估计方法。该方法的反应曲线非参数建模依赖于具有共享隐藏神经元的径向基函数网络，从而促进了对治疗结果共性的建模。估计与推断方案在贝叶斯框架下开发，并通过高效的马尔可夫链蒙特卡洛算法实现，能适当容纳分析中所有方面的不确定性。通过模拟实验展示了该方法的数值性能。将所提方法应用于MIMIC数据后，我们获得了关于不同治疗策略对出院脓毒症患者ICU住院时长及12小时SOFA评分影响的若干有趣发现。