Title: Bayesian nonparametric mixtures of categorical directed graphs for heterogeneous causal inference Show Chinese title

Title: 用于异质因果推断的贝叶斯非参数分类有向图混合模型

Abstract: Quantifying causal effects of exposures on outcomes, such as a treatment and a disease respectively, is a crucial issue in medical science for the administration of effective therapies. Importantly, any related causal analysis should account for all those variables, e.g. clinical features, that can act as risk factors involved in the occurrence of a disease. In addition, the selection of targeted strategies for therapy administration requires to quantify such treatment effects at personalized level rather than at population level. We address these issues by proposing a methodology based on categorical Directed Acyclic Graphs (DAGs) which provide an effective tool to infer causal relationships and causal effects between variables. In addition, we account for population heterogeneity by considering a Dirichlet Process mixture of categorical DAGs, which clusters individuals into homogeneous groups characterized by common causal structures, dependence parameters and causal effects. We develop computational strategies for Bayesian posterior inference, from which a battery of causal effects at subject-specific level is recovered. Our methodology is evaluated through simulations and applied to a dataset of breast cancer patients to investigate cardiotoxic side effects that can be induced by the administrated anticancer therapies. Show Chinese abstract

Abstract: 量化暴露对结果的影响，例如治疗和疾病分别，是医学科学中管理有效疗法的一个关键问题。重要的是，任何相关的因果分析都应考虑所有可能作为疾病发生风险因素的变量，例如临床特征。此外，治疗方案的选择需要在个性化层面而非总体层面量化这种治疗效果。我们通过提出一种基于分类有向无环图（DAG）的方法来解决这些问题，这些DAG提供了一种有效的工具，用于推断变量之间的因果关系和因果效应。此外，我们通过考虑分类DAG的狄利克雷过程混合模型来考虑人群异质性，该模型将个体聚类到具有共同因果结构、依赖参数和因果效应的同质组中。我们开发了用于贝叶斯后验推断的计算策略，从中恢复出特定于受试者的多种因果效应。我们的方法通过模拟进行评估，并应用于乳腺癌患者的数据集，以研究 administered 抗癌疗法可能引起的心脏毒性副作用。