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

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  • Hongying Li
  • Lei Wang

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.

Suggested Citation

  • Hongying Li & Lei Wang, 2026. "Policy Targeting with Binary Classification Trees: an Application to Rural Hospital Closures," Papers 2609.13068, arXiv.org, revised Sep 2026.
    • Handle: RePEc:arx:papers:2609.13068
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    References listed on IDEAS

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