论文标题
估计个性化治疗效果的合奏方法
Ensemble Method for Estimating Individualized Treatment Effects
论文作者
论文摘要
在许多医疗和业务应用中，研究人员有兴趣使用随机实验的数据估算个性化的治疗效果。例如，在医疗应用中，医生从临床试验和技术公司中学习治疗效果，研究人员从A/B测试实验中学习了治疗效果。尽管已经为此任务提出了数十个机器学习模型，但由于无法观察到地面实际治疗效果，因此确定哪种模型最适合手头问题是挑战。与最新的几篇论文提出了选择这些竞争模型之一的方法相反，我们提出了一种算法，用于汇总来自不同模型库的估计值。我们将结合与43个基准数据集中的模型选择进行比较，并发现结合每次都会获胜。从理论上讲，我们证明我们的集合模型（渐近地）至少与正在考虑的最佳模型一样准确，即使允许候选模型的数量随样本量增长。
In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment. For example in medical applications, doctors learn the treatment effects from clinical trials and in technology companies, researchers learn them from A/B testing experiments. Although dozens of machine learning models have been proposed for this task, it is challenging to determine which model will be best for the problem at hand because ground-truth treatment effects are unobservable. In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.
