# Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

## TL;DR

This paper introduces a generative model for identifying interpretable subgroups with different treatment effects in observational data, aiding in the development of better opioid prescribing guidelines.

## Contribution

It proposes a novel mixture model that discovers subgroups with varied causal effects, combining interpretability with nonlinear outcome prediction.

## Key findings

- Identifies subgroups with distinct treatment responses related to opioid outcomes.
- Enhances interpretability through sparsity in the mixture model.
- Provides insights that can inform safer opioid prescribing practices.

## Abstract

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision support