Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare problems that greatly hampered research on developing effective treatment and understanding of the underlying neurobiological mechanism. Very few studies have been conducted to investigate heterogeneous causal effects (HCEs) in graphical contexts due to the lack of statistical methods. To characterize this heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators. Such confounders with an interaction with the treatment are known as moderators. This allows us to flexibly produce HCGs given different moderators and explicitly characterize HCEs from the treatment or potential mediators on the outcome. We establish the theoretical forms of HCEs and derive their properties at the individual level in both linear and nonlinear models. An interactive structural learning is developed to estimate the complex HCGs and HCEs with confidence intervals provided. Our method is empirically justified by extensive simulations and its practical usefulness is illustrated by exploring causality among psychiatric disorders for trauma survivors.

翻译：异质性与共病性是与多种医疗问题交织的两大挑战，极大地阻碍了有效治疗方案开发及对潜在神经生物学机制的理解。由于缺乏统计方法，目前鲜有研究探讨图模型背景下的异质性因果效应。为刻画这种异质性，我们首先通过泛化含有混杂因子交互作用及多重中介变量的因果图模型，提出了异质性因果图的概念。这类与处理存在交互作用的混杂因子被称为调节变量。基于不同调节变量，该方法能灵活生成异质性因果图，并明确刻画治疗或潜在中介变量对结局的异质性因果效应。我们建立了异质性因果效应的理论形式，并在线性与非线性模型两个层面上推导了其在个体层面的性质。进一步地，我们提出了一种交互式结构学习算法，用于估计复杂异质性因果图及异质性因果效应，并提供置信区间。通过大量模拟实验验证了方法的有效性，并以创伤幸存者精神障碍间的因果分析为例，展示了其在实践中的应用价值。