A PhD thesis proposes three methodological innovations and a design framework for bringing causal machine learning into clinical practice, testing continuous treatment models on a physiotherapy dosage case
This PhD thesis addresses how causal machine learning can be translated into real-world clinical practice along three lines: for binary treatment effect estimation in clinical contexts it identifies eight key decision points that directly influence treatment effect estimates and proposes a structured framework for designing causal models in clinical settings; for the difficulty of verifying counterfactual predictions it proposes a novel method that grounds average model predictions in a group-level quantity that can be empirically verified using randomized or real-world clinical trial data, and shows that a model with a lower counterfactual error bound can be constructed and that information bottleneck regularization improves treatment effect estimation; for continuous causal machine learn