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Estimating complier average causal effects for clustered RCTs when the treatment affects the service population

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  • Schochet Peter Z.

    (Senior Fellow and Associate Director, Mathematica, P.O. Box 2393, Princeton, NJ, 08543-2393., USA)

Abstract

Randomized controlled trials (RCTs) sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting (IPW) estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply (reduce) to nonclustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children’s cognitive development scores. An R program for estimation is available for download.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:causin:v:10:y:2022:i:1:p:300-334:n:1
    DOI: 10.1515/jci-2022-0033
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    References listed on IDEAS

    as
    1. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    2. Peter Z. Schochet, 2020. "The Complier Average Causal Effect Parameter for Multiarmed RCTs," Evaluation Review, , vol. 44(5-6), pages 410-436, October.
    3. Johnson, Helen & McNally, Sandra & Rolfe, Heather & Ruiz-Valenzuela, Jenifer & Savage, Robert & Vousden, Janet & Wood, Clare, 2019. "Reprint of: Teaching assistants, computers and classroom management," Labour Economics, Elsevier, vol. 59(C), pages 17-32.
    4. repec:mpr:mprres:5863 is not listed on IDEAS
    5. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    6. Johnson, Helen & McNally, Sandra & Rolfe, Heather & Ruiz-Valenzuela, Jenifer & Savage, Robert & Vousden, Janet & Wood, Clare, 2019. "Teaching assistants, computers and classroom management," Labour Economics, Elsevier, vol. 58(C), pages 21-36.
    7. repec:mpr:mprres:5406 is not listed on IDEAS
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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