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Treatment Effects of Multi-Valued Treatments in Hyper-Rectangle Model

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  • Xunkang Tian

Abstract

This study investigates the identification of marginal treatment responses within multi-valued treatment models. Extending the hyper-rectangle model introduced by Lee and Salanie (2018), this paper relaxes restrictive assumptions, including the requirement of known treatment selection thresholds and the dependence of treatments on all unobserved heterogeneity. By incorporating an additional ranked treatment assumption, this study demonstrates that the marginal treatment responses can be identified under a broader set of conditions, either point or set identification. The framework further enables the derivation of various treatment effects from the marginal treatment responses. Additionally, this paper introduces a hypothesis testing method to evaluate the effectiveness of policies on treatment effects, enhancing its applicability to empirical policy analysis.

Suggested Citation

  • Xunkang Tian, 2025. "Treatment Effects of Multi-Valued Treatments in Hyper-Rectangle Model," Papers 2509.05177, arXiv.org.
    • Handle: RePEc:arx:papers:2509.05177
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    References listed on IDEAS

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    6. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
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