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Filtered and Unfiltered Treatment Effects with Targeting Instruments

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  • Salanié, Bernard
  • Lee, Sokbae

Abstract

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. We establish conditions under which counterfactual averages and treatment effects are identified for heterogeneous complier groups. These conditions require a combination of assumptions that restrict both the unobserved heterogeneity in treatment assignment and how the instruments target the treatments. We introduce the concept of filtered treatment, which takes into account limitations in the analyst’s information. Finally, we illustrate the usefulness of our framework by applying it to data from the Student Achievement and Retention Project and the Head Start Impact Study.

Suggested Citation

  • Salanié, Bernard & Lee, Sokbae, 2020. "Filtered and Unfiltered Treatment Effects with Targeting Instruments," CEPR Discussion Papers 15092, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:15092
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    References listed on IDEAS

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    1. Sokbae Lee & Bernard Salanié, 2018. "Identifying Effects of Multivalued Treatments," Econometrica, Econometric Society, vol. 86(6), pages 1939-1963, November.
    2. Kamat, Vishal, 2019. "Identification with Latent Choice Sets," TSE Working Papers 19-1031, Toulouse School of Economics (TSE).
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    4. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    5. Philip Oreopoulos & Daniel Lang & Joshua Angrist, 2009. "Incentives and Services for College Achievement: Evidence from a Randomized Trial," American Economic Journal: Applied Economics, American Economic Association, vol. 1(1), pages 136-163, January.
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    10. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    11. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2014. "Asymptotic Efficiency of Semiparametric Two-step GMM," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 919-943.
    12. Huang, Liquan & Khalil, Umair & Yıldız, Neşe, 2019. "Identification and estimation of a triangular model with multiple endogenous variables and insufficiently many instrumental variables," Journal of Econometrics, Elsevier, vol. 208(2), pages 346-366.
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    Cited by:

    1. Heinesen, Eskil & Hvid, Christian & Kirkebøen, Lars & Leuven, Edwin & Mogstad, Magne, 2022. "Instrumental variables with unordered treatments: Theory and evidence from returns to fields of study," Memorandum 3/2022, Oslo University, Department of Economics.
    2. Ferreyra,Maria Marta & Galindo,Camila & Urzúa,Sergio, 2021. "Labor Market Effects of Short-Cycle Higher Education Programs : Lessons from Colombia," Policy Research Working Paper Series 9717, The World Bank.
    3. Humlum, Anders & Munch, Jakob R. & Rasmussen, Mette, 2023. "What Works for the Unemployed? Evidence from Quasi-Random Caseworker Assignments," IZA Discussion Papers 16033, Institute of Labor Economics (IZA).

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