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

Xunkang Tian

arXiv 5 Sep 2025 · Econometrics

arXiv:2509.05177 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Lee, S. and B. Salanié (2018) Identifying effects of multivalued treatments1.00084100%
2Heckman, J. J. and R. Pinto (2018) Unordered monotonicity0.64422100%
3Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects0.64422100%
4Mogstad, M., A. Santos, and A. Torgovitsky (2018) Using instrumental variables for inference about policy relevant treatment parameters0.64422100%
5Imbens, G. W. and C. F. Manski (2004) Confidence intervals for partially identified parameters0.5113233%
6Abadie, A (2002) Bootstrap tests for distributional treatment effects in instrumental variable models0.40511100%
7Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models0.40511100%
8Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of causal effects using instrumental variables0.40511100%
9Beresteanu, A. and F. Molinari (2008) Asymptotic properties for a class of partially identified models0.40511100%
10Cattaneo, M. D (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability0.40511100%

Showing the top 10 of 25 scored citations.