Policy Targeting with Binary Classification Trees: an Application to Rural Hospital Closures
Year: 2026
Type: preprint
Abstract: Empirical researchers often use binary classification trees to identify subgroups at risk of adverse outcomes. We compare two classification tree algorithms in this policy-targeting context: classical classification and regression trees (CART) and the maximizing-distance final-split approach (MDFS). We establish a theoretical setting in which MDFS identifies a high-risk subgroup while CART identifies none. Applied to rural hospital closure data, MDFS targets a high-closure-probability subgroup: for-profit hospitals with more than 63 days in accounts receivable. CART misses this subgroup. To facilitate further application of these classification tree algorithms, we provide \texttt{targetree} package in Python, R, and Stata.
Source: arXiv (Cornell University)
Authors: Li, Hongying, Wang, Lei
Institution: The Ohio State University
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7212734119
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