Modelling multiple outcomes using tree-based methods in causal inference

在因果推理中使用基于树的方法对多个结果进行建模

• 批准号: 2576152
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2576152
• 关键词: Modelling; multiple; outcomes; using; tree

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.

在医学统计学中，因果推理是研究治疗对健康结果的直接影响的过程。在实践中，只实施了一种治疗，所以我们需要得出反事实的结论，即如果给予另一种治疗，我们会观察到什么。此外，治疗往往不是随机分配的，而是基于受试者的病史记录，这给区分疾病严重程度和治疗结果带来了困难。这些是因果推理的一些基本挑战。最近，用于个体治疗效果因果推理的统计学习方法取得了重大进展，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不止一种结果来决定的，通常是关注的主要结果与治疗的不良副作用。例如，心脏病的风险与出血的风险（治疗的副作用）。这就是为什么许多研究收集不止一种结果的数据，试图同时探索对多种结果的影响。该项目将建立在目前基于树的因果推理方法的基础上，探索它们在同时建模多个结果中的应用。目的是扩展贝叶斯因果森林，为结果建立一个复合联合模型，以推断治疗效果的个人水平估计。其中一个鼓舞人心的例子是使用血液稀释剂（抗血小板治疗）来降低患者患心脏病的风险，这些患者可能也有出血的高风险（治疗的副作用）。预计这项工作将带来方法学研究和应用医学成果的结合。它将在几个医疗保健示例中与最广泛使用的统计计算语言R比较方法的性能。