Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models

TL;DR

The paper introduces LI-ITR, a novel method combining flexible machine learning with local interpretability to derive patient-specific treatment rules, enhancing precision medicine with transparent decision explanations.

Contribution

LI-ITR uniquely integrates local synthetic data generation and interpretable experts to improve individualized treatment rule estimation in complex healthcare settings.

Findings

Accurately recovers true local treatment coefficients

Effectively estimates optimal patient-specific treatment strategies

Demonstrates practical utility in breast cancer treatment management

Abstract

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.