title: Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution.
abstract: 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.
authors.affiliation: University of Wisconsin, Madison, WI.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Weiss
authors.affiliation: University of Wisconsin, Madison, WI.
authors.forename: Finn
authors.initials: F
authors.lastname: Kuusisto
authors.affiliation: University of Wisconsin, Madison, WI.
authors.forename: Kendrick
authors.initials: K
authors.lastname: Boyd
authors.affiliation: University of Washington, Seattle, WA.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: University of Wisconsin, Madison, WI.
authors.forename: David
authors.initials: D
authors.lastname: Page
mesh_terms: D000465:Algorithms; D006801:Humans; D000069550:Machine Learning; D012306:Risk