On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
This work addresses the challenge of understanding heterogeneous treatment effects in healthcare, particularly for conditions with comorbidity, offering a novel statistical approach for causal inference in graphical contexts.
The authors tackled the problem of estimating heterogeneous causal effects in complex graphical models with confounder interactions and multiple mediators, developing a method that provides theoretical forms and confidence intervals for these effects, validated through simulations and applied to psychiatric disorders in trauma survivors.
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.