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

by   Chirag Nagpal, et al.

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


Heterogeneous Treatment Effect Estimation through Deep Learning

Estimating heterogeneous treatment effect is an important task in causal...

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

The practical utility of causality in decision-making is widespread and ...

Bayesian variable selection in hierarchical difference-in-differences models

A popular method for estimating a causal treatment effect with observati...

An improved neural network model for treatment effect estimation

Nowadays, in many scientific and industrial fields there is an increasin...

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

Estimating personalized treatment effects from high-dimensional observat...

Please sign up or login with your details

Forgot password? Click here to reset