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：异质性和共病性是诸多医疗问题中相互交织的两大挑战，严重阻碍了有效治疗方案研发及其潜在神经生物学机制的研究。由于统计方法的匮乏，目前极少有研究探讨图环境下的异质因果效应（HCEs）。为刻画这种异质性，我们首先通过引入基于混杂变量的交互项与多重中介变量，对因果图模型进行泛化，从而提出异质性因果图（HCGs）的概念。这类与治疗产生交互作用的混杂变量被称为调节变量。由此，我们能够根据不同的调节变量灵活生成HCGs，并明确刻画治疗或潜在中介变量对结局的HCEs。我们建立了HCEs的理论形式，并推导了其在个体层面在线性与非线性模型中的性质。继而开发了一种交互式结构学习算法，用于估计复杂的HCGs与HCEs，并提供置信区间。通过大量模拟实验验证了该方法的实证有效性，并通过对创伤幸存者精神障碍间的因果关系分析展示了其实用价值。