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The Complier Average Causal Effect Parameter for Multiarmed RCTs

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  • Peter Z. Schochet

Abstract

In randomized controlled trials, the complier average causal effect (CACE) parameter is often of policy interest because it pertains to intervention effects for study units that comply with their research assignments and receive a meaningful dose of treatment services. Causal inference methods for identifying and estimating the CACE parameter using an instrumental variables (IV) framework are well established for designs with a single treatment and control group. This article uses a parallel IV framework to discuss and build on the much smaller literature on estimation of CACE parameters for designs with multiple treatment groups. The key finding is that the conditions to identify and estimate CACE parameters are much more complex for multiarmed designs and may not be tractable in some cases. Practical steps are provided on how to proceed, and a case study demonstrates key issues. The results suggest that ensuring compliance is particularly important in multiarmed trials so that intention-to-treat estimates on the offer of intervention services (which can be identified) can provide meaningful information on the CACE parameters.

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  • Peter Z. Schochet, 2020. "The Complier Average Causal Effect Parameter for Multiarmed RCTs," Evaluation Review, , vol. 44(5-6), pages 410-436, October.
    • Handle: RePEc:sae:evarev:v:44:y:2020:i:5-6:p:410-436
    DOI: 10.1177/0193841X20979205
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    as
    1. Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    3. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    4. Castleman, Benjamin L. & Page, Lindsay C., 2015. "Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low-income high school graduates?," Journal of Economic Behavior & Organization, Elsevier, vol. 115(C), pages 144-160.
    5. repec:mpr:mprres:7071 is not listed on IDEAS
    6. repec:mpr:mprres:6574 is not listed on IDEAS
    7. Kraft, Matthew A. & Rogers, Todd, 2015. "The underutilized potential of teacher-to-parent communication: Evidence from a field experiment," Economics of Education Review, Elsevier, vol. 47(C), pages 49-63.
    8. André Lapidus & Jean-Sébastien Lenfant & Goulven Rubin & Hans-Michael Trautwein, 2020. "Introduction," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 27(6), pages 815-818, November.
    9. Tan, Zhiqiang, 2010. "Marginal and Nested Structural Models Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 157-169.
    10. Dana Rotz & Paul Burkander & Mary Grider & Kenneth Fortson & Linda Molinari & Elias Sanchez-Eppler & Lindsay Cattell, "undated". "Providing Public Workforce Services to Job Seekers: 30-Month Impact Findings on the WIA Adult and Dislocated Worker Programs, Technical Supplement," Mathematica Policy Research Reports db04b33db50a4d45824da5a65, Mathematica Policy Research.
    11. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    12. Matthew Blackwell, 2017. "Instrumental Variable Methods for Conditional Effects and Causal Interaction in Voter Mobilization Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 590-599, April.
    13. Jing Cheng & Dylan S. Small, 2006. "Bounds on causal effects in three‐arm trials with non‐compliance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 815-836, November.
    14. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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    Cited by:

    1. Schochet Peter Z., 2022. "Estimating complier average causal effects for clustered RCTs when the treatment affects the service population," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 300-334, January.

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