DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation

TL;DR

DISCRET is a novel self-interpretable framework for individual treatment effect estimation that synthesizes faithful, rule-based explanations, achieving accuracy comparable to black-box models while providing transparent reasoning.

Contribution

It introduces a new RL-based method to generate faithful explanations for ITE models, balancing interpretability and accuracy.

Findings

Outperforms existing self-interpretable models

Achieves accuracy comparable to black-box models

Provides faithful, rule-based explanations

Abstract

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024.