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:与各种保健问题相关的两个相互交织的挑战,与各种保健问题有关,严重妨碍了关于发展有效治疗和了解基本神经生物机制的研究,由于缺乏统计方法,几乎没有开展什么研究来调查图形环境中的因果效应(HCEs),因为缺乏统计方法。为了描述这种异质性,我们首先将因果因果图(HCGs)概念化,将因果图形模型与基于混杂的相互作用和多个调解人加以概括,这些与治疗互动的混杂者被称为主持人。这使我们能够灵活地为不同的主持人制作HCGs,并明确描述治疗或潜在调解人对结果的HCEs特征。我们建立了HCE的理论形式,并在个人层面以线性和非线性模式得出其特性。我们开发了互动结构学习,以根据经验估计复杂的HCGs和HCEs与提供的信任期。我们的方法有广泛的模拟,其实际用途是通过探讨创伤幸存者的精神病的因果关系来说明的。