Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
This work addresses the need for interpretable and personalized treatment decisions in healthcare, offering a method that improves upon existing approaches by providing both flexibility and transparency, though it is incremental in nature.
The paper tackles the problem of balancing accuracy and interpretability in individualized treatment rules (ITRs) for healthcare by introducing the LI-ITR method, which combines flexible machine learning models with locally interpretable approximations to construct subject-specific treatment rules, as demonstrated in simulation studies and an application to breast cancer side-effect management.
Individualized treatment rules (ITRs) aim to optimize healthcare by tailoring treatment decisions to patient-specific characteristics. Existing methods typically rely on either interpretable but inflexible models or highly flexible black-box approaches that sacrifice interpretability; moreover, most impose a single global decision rule across patients. We introduce the Locally Interpretable Individualized Treatment Rule (LI-ITR) method, which combines flexible machine learning models to accurately learn complex treatment outcomes with locally interpretable approximations to construct subject-specific treatment rules. LI-ITR employs variational autoencoders to generate realistic local synthetic samples and learns individualized decision rules through a mixture of interpretable experts. Simulation studies show that LI-ITR accurately recovers true subject-specific local coefficients and optimal treatment strategies. An application to precision side-effect management in breast cancer illustrates the necessity of flexible predictive modeling and highlights the practical utility of LI-ITR in estimating optimal treatment rules while providing transparent, clinically interpretable explanations.