Modelling multiple outcomes using tree-based methods in causal inference

在医学统计学中，因果推断是研究治疗对感兴趣的健康结果的直接影响的过程。在实践中，只有一种治疗方法被实施，所以我们需要得出反事实的结论，即如果给予另一种治疗方法，我们会观察到什么。此外，治疗通常不是随机分配的，而是基于受试者的病史记录，这在将疾病严重程度与治疗结果分开方面造成了困难。这些是因果推理的一些基本挑战。最近，个体治疗效果因果推理的统计学习方法取得了显着进展，部分原因是电子健康记录等大型数据集的可用性。这些因果推理模型通常研究治疗对单一结果的影响。然而，治疗分配通常是根据一个以上的结果决定的，通常是主要的利益与治疗的不良副作用。例如，心脏病风险与出血风险（治疗副作用）。这就是为什么许多研究收集了不止一个结果的数据，试图同时探索对多个结果的影响。该项目将以目前关于因果推理中基于树的方法的工作为基础，探索其在同时模拟多种结果方面的应用。其目的是扩展贝叶斯因果森林建立一个复合联合模型的结果，以推断个人水平的估计治疗效果。其中一个令人鼓舞的例子是使用血液稀释剂（抗血小板疗法）来减轻可能也有出血（治疗副作用）高风险的患者患心脏病的风险。预计这项工作将导致方法学研究和应用医学成果的结合。它将比较几个医疗保健例子与统计计算最广泛使用的语言- R的方法的性能。

In medical statistics, causal inference is the process of studying the direct effects of a treatment on a health outcome of interest. In practice, only one treatment is administered, so we need to draw counterfactual conclusions about what we would have observed if another one had been given. Furthermore, treatment is often not allocated randomly but based on subjects' medical history records, and this creates difficulties in separating disease severity from the treatment outcome. These are some of the fundamental challenges of causal inference.Recently the statistical learning methods for causal inference of individual treatment effects have seen significant advances, partly due to the availability of large datasets such as electronic health records. These causal inference models typically study the effect of a treatment on a single outcome. However, treatment allocation is often decided based on more than one outcome, commonly the main outcome of interest versus adverse side-effects of treatment. For example, the risk of heart disease versus the risk of bleeding (side-effect from the treatment). That is why, many studies collect data on more than one outcome, seeking to explore the effects on multiple outcomes simultaneously. The project would build on the current work on tree-based methods in causal inference to explore their application in modelling multiple outcomes simultaneously. The aim is to extend Bayesian Causal Forests to build a composite joint model for the outcomes to infer individual-level estimates of the effect of treatment. One of the motivating examples is the use of blood-thinners (anti-platelet therapy) to mitigate heart disease risk on patients who might also be at high risk of bleeding (side-effect of the treatment).It is expected that the work will lead to a mix of methodological research as well as applied medical results. It would compare the performance of the methods on several healthcare examples with the most widely used language for statistical computing - R.