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Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction

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  • Martin E Andresen
  • Martin Huber

Abstract

SummaryWhen estimating local average and marginal treatment effects using instrumental variables (IVs), multivalued endogenous treatments are frequently converted to binary measures, supposedly to improve interpretability or policy relevance. Such binarisation introduces a violation of the IV exclusion if (a) the IV affects the multivalued treatment within support areas below and/or above the threshold and (b) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with a binarised treatment and permit identifying the average effect of (a) the binarised treatment and (b) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests which we apply to the estimation of the returns to college graduation instrumented by college proximity.

Suggested Citation

  • Martin E Andresen & Martin Huber, 2021. "Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 536-558.
    • Handle: RePEc:oup:emjrnl:v:24:y:2021:i:3:p:536-558.
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    File URL: http://hdl.handle.net/10.1093/ectj/utab002
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    Cited by:

    1. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org.
    2. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    3. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    4. Kyunghoon Ban & Désiré Kédagni, 2022. "Nonparametric bounds on treatment effects with imperfect instruments [Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 477-493.
    5. Didier Nibbering & Matthijs Oosterveen, 2023. "Instrument-based estimation of full treatment effects with movers," Papers 2306.07018, arXiv.org.
    6. Tymon Słoczyński & S. Derya Uysal & Jeffrey M. Wooldridge, 2025. "Abadie’s Kappa and Weighting Estimators of the Local Average Treatment Effect," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 164-177, January.
    7. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    8. Martin Eckhoff Andresen & Sturla A. Løkken, 2019. "High school dropout for marginal students. Evidence from randomized exam form," Discussion Papers 894, Statistics Norway, Research Department.
    9. Jansson, Joakim & Petterson-Lidbom, Per & Priks, Mikael & Tyrefors, Björn, 2025. "Sentence Length and Recidivism: Court Rulings based on BAC," Working Paper Series 1536, Research Institute of Industrial Economics.
    10. Baltagi, Badi H. & Flores-Lagunes, Alfonso & Karatas, Haci M., 2023. "The effect of higher education on Women's obesity and smoking: Evidence from college openings in Turkey," Economic Modelling, Elsevier, vol. 123(C).
    11. Joppe de Ree & Matthijs Oosterveen & Dinand Webbink, 2023. "The quality of school track assignment decisions by teachers," Papers 2304.10636, arXiv.org, revised Jun 2025.
    12. Elisa Gerten & Michael Beckmann & Elisa Gerten & Matthias Kräkel, 2022. "Information and Communication Technology, Hierarchy, and Job Design," ECONtribute Discussion Papers Series 189, University of Bonn and University of Cologne, Germany.
    13. Castillo, Marco & Linardi, Sera & Petrie, Ragan, 2024. "Recidivism and Barriers to Reintegration: A Field Experiment Encouraging Use of Reentry Support," IZA Discussion Papers 17522, Institute of Labor Economics (IZA).
    14. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    15. Nibbering, Didier & Oosterveen, Matthijs & Silva, Pedro Luís, 2022. "Clustered Local Average Treatment Effects: Fields of Study and Academic Student Progress," IZA Discussion Papers 15159, Institute of Labor Economics (IZA).
    16. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    17. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    18. Christophe Bruneel-Zupanc, 2023. "Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs," Papers 2303.12667, arXiv.org, revised Sep 2025.
    19. Stefan Tübbicke, 2023. "When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college," Empirical Economics, Springer, vol. 65(6), pages 2979-2999, December.
    20. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.

    More about this item

    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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