CODE - XAI: Construing and Deciphering Treatment Effects via Explainable AI using Real-world Data

Abstract

Determining which features drive the treatment effect for individual patients has long been a complex and critical question in clinical decision-making. Evidence from randomized controlled trials (RCTs) are the gold standard for guiding treatment decisions. However, individual patient differences often complicate the application of RCT findings, leading to imperfect treatment options. Traditional subgroup analyses fall short due to data dimensionality, type, and study design. To overcome these limitations, we propose CODE-XAI, a framework that interprets Conditional Average Treatment Effect (CATE) models using Explainable AI (XAI) to perform feature discovery. CODE-XAI provides feature attribution at the individual subject level, enhancing our understanding of treatment responses. We benchmark these XAI methods using semi-synthetic data and RCTs, demonstrating their effectiveness in uncovering feature contributions and enabling cross-cohort analysis, advancing precision medicine and scientific discovery.