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Testing Exclusion and Shape Restrictions in Potential Outcomes Models

Hiroaki Kaido, Kirill Ponomarev

arXiv 24 Dec 2025 · Econometrics

arXiv:2512.20851 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Exclusion and shape restrictions play a central role in defining causal effects and interpreting estimates in potential outcomes models. To date, the testable implications of such restrictions have been studied on a case-by-case basis in a limited set of models. In this paper, we develop a general framework for characterizing sharp testable implications of general support restrictions on the potential response functions, based on a novel graph-based representation of the model. The framework provides a unified and constructive method for deriving all observable implications of the modeling assumptions. We illustrate the approach in several popular settings, including instrumental variables, treatment selection, mediation, and interference. As an empirical application, we revisit the US Lung Health Study and test for the presence of spillovers between spouses, specification of exposure maps, and persistence of treatment effects over time.

Citation extraction

88
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187
in-text mentions
88
distinct cited
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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
1Yuehao Bai and Max Tabord-Meehan (2024) Sharp Testable Implications of Encouragement Designs1.00073100%
2Artstein, Zvi (1983) Distributions of random sets and random selections1.00053100%
3Kwon, Soonwoo and Roth, Jonathan (2024) Testing Mechanisms0.96510590%
4Victor Chernozhukov and Sokbae Lee and Adam M. Rosen (2013) INTERSECTION BOUNDS: ESTIMATION AND INFERENCE0.9416383%
5Donald W. K. Andrews and Gustavo Soares (2010) Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection0.92843100%
6Toru Kitagawa (2015) A Test For Instrument Validity0.9098475%
7Mogstad, Magne and Torgovitsky, Alexander and Walters, Christopher R (2021) The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables0.87462100%
8Imbens, Guido W and Angrist, Joshua D (1994) Identification and Estimation of Local Average Treatment Effects0.8434375%
9Molchanov, Ilya and Molinari, Francesca (2018) Random sets in econometrics0.8434375%
10Manski, Charles F (2013) Identification of treatment response with social interactions0.81142100%

Showing the top 10 of 88 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1On the falsification of instrumental variable models for heterogeneous treatment effects0.81142