Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.

Journal Article (Journal Article)

Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the individualized treatment effect (ITE), which has better applicability to new patients. We compare ATE-estimation using randomized and observational analysis methods against ITE-estimation using machine learning, and describe how the ITE theoretically generalizes to new population distributions, whereas the ATE may not. On a synthetic data set of statin use and myocardial infarction (MI), we show that a learned ITE model improves true ITE estimation and outperforms the ATE. We additionally argue that ITE models should be learned with a consistent, nonparametric algorithm from unweighted examples and show experiments in favor of our argument using our synthetic data model and a real data set of D-penicillamine use for primary biliary cirrhosis.

Full Text

Duke Authors

Cited Authors

  • Weiss, J; Kuusisto, F; Boyd, K; Liu, J; Page, D

Published Date

  • 2015

Published In

Volume / Issue

  • 2015 /

Start / End Page

  • 1306 - 1315

PubMed ID

  • 26958271

Pubmed Central ID

  • PMC4765638

Electronic International Standard Serial Number (EISSN)

  • 1942-597X


  • eng

Conference Location

  • United States