IDEAS home Printed from https://ideas.repec.org/a/sae/evarev/v44y2020i5-6p410-436.html
   My bibliography  Save this article

The Complier Average Causal Effect Parameter for Multiarmed RCTs

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0193841X20979205
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0193841X20979205?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    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. 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.
    4. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    5. 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.
    6. repec:mpr:mprres:7071 is not listed on IDEAS
    7. 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.
    8. repec:mpr:mprres:6574 is not listed on IDEAS
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Z. Schochet, "undated". "Multi-Armed RCTs: A Design-Based Framework," Mathematica Policy Research Reports eedf2eac4d4c4d8e869052c1d, Mathematica Policy Research.
    2. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    3. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    4. Rachel Axelrod & Daniel Nevo, 2023. "A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2743-2756, September.
    5. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    6. Susan Athey & Raj Chetty & Guido Imbens, 2020. "Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes," Papers 2006.09676, arXiv.org.
    7. Plamen Nikolov & Hongjian Wang & Kevin Acker, 2020. "Wage premium of Communist Party membership: Evidence from China," Pacific Economic Review, Wiley Blackwell, vol. 25(3), pages 309-338, August.
    8. Undral Byambadalai, 2022. "Identification and Inference for Welfare Gains without Unconfoundedness," Papers 2207.04314, arXiv.org.
    9. Dinkelman, Taryn & Berlinski, Samuel & Busso, Matias & Martinez A., Claudia, 2021. "Reducing Parent-School Information Gaps and Improving Education Outcomes: Evidence from High-Frequency Text Messages," CEPR Discussion Papers 15949, C.E.P.R. Discussion Papers.
    10. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    11. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    12. Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli, 2023. "Bayesian inference for a principal stratum estimand on recurrent events truncated by death," Biometrics, The International Biometric Society, vol. 79(4), pages 3792-3802, December.
    13. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    14. Bingbo Gao & Jianyu Yang & Ziyue Chen & George Sugihara & Manchun Li & Alfred Stein & Mei-Po Kwan & Jinfeng Wang, 2023. "Causal inference from cross-sectional earth system data with geographical convergent cross mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    15. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    16. Peter Z. Schochet, 2018. "Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 568-593, October.
    17. Chiba, Yasutaka, 2012. "A note on bounds for the causal infectiousness effect in vaccine trials," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1422-1429.
    18. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    19. Taguri Masataka & Chiba Yasutaka, 2012. "Instruments and Bounds for Causal Effects under the Monotonic Selection Assumption," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-23, August.
    20. Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute of Labor Economics (IZA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:evarev:v:44:y:2020:i:5-6:p:410-436. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.