Policy Targeting with Binary Classification Trees: an Application to Rural Hospital Closures

Hongying Li and Lei Wang

Papers from arXiv.org

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.

Date: 2026-09, Revised 2026-09
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