Approaches to Causal Inference in Precision Medicine: AI-based Causal Modeling vs Latent model

Music City Center 

Causal inference plays an important role in precision medicine, which estimates the treatment effect for the "right" patients within a heterogeneous disease population. Artificial intelligence (AI) has emerged as a powerful tool for advancing causal inference, enabling the identification of subgroups where treatment effects vary significantly. This paper explores the integration of AI-based causal effect modeling for subgroup identification.
Our approach employs causal machine learning methods, such as causal forests, to estimate Conditional Average Treatment Effects (CATE) for individual latent subgroups characterized by features such as demographics, baseline disease characteristics, genetic markers, and outcomes. These methods go beyond traditional regression-based inference by modeling complex interactions and nonlinear relationships between features and treatment effects.
Applications of the methodology are demonstrated through simulation study on disease progression endpoints in a clinical trial, where AI-based causal modeling will be compared to traditional latent model approach in identifying patient subgroups that benefit most from a novel treatment

Causal inference

AI-based

Precision medicine

Subgroup Identification

latent model

disease progression 

Biopharmaceutical Section