AI RESEARCH

Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes

arXiv CS.AI

ArXi:2503.11477v2 Announce Type: replace Understanding the factors that trigger or prevent undesirable health outcomes across patient subpopulations is essential for designing targeted interventions. While randomized controlled trials and expert-led patient interviews are standard methods for identifying these factors, they can be time-consuming or infeasible. Causal discovery offers an alternative to conventional approaches by generating cause-and-effect hypotheses from observational data, yet its practical utility is limited by strong or untestable assumptions.