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Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification

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  • Laura Forastiere
  • Fabrizia Mealli
  • Tyler J. VanderWeele

Abstract

Exploration of causal mechanisms is often important for researchers and policymakers to understand how an intervention works and how it can be improved. This task can be crucial in clustered encouragement designs (CEDs). Encouragement design studies arise frequently when the treatment cannot be enforced because of ethical or practical constraints and an encouragement intervention (information campaigns, incentives, etc.) is conceived with the purpose of increasing the uptake of the treatment of interest. By design, encouragements always entail the complication of noncompliance. Encouragements can also give rise to a variety of mechanisms, particularly when encouragement is assigned at the cluster level. Social interactions among units within the same cluster can result in spillover effects. Disentangling the effect of encouragement through spillover effects from that through the enhancement of the treatment would give better insight into the intervention and it could be compelling for planning the scaling-up phase of the program. Building on previous works on CEDs and noncompliance, we use the principal stratification framework to define stratum-specific causal effects, that is, effects for specific latent subpopulations, defined by the joint potential compliance statuses under both encouragement conditions. We show how the latter stratum-specific causal effects are related to the decomposition commonly used in the literature and provide flexible homogeneity assumptions under which an extrapolation across principal strata allows one to disentangle the effects. Estimation of causal estimands can be performed with Bayesian inferential methods using hierarchical models to account for clustering. We illustrate the proposed methodology by analyzing a cluster randomized experiment implemented in Zambia and designed to evaluate the impact on malaria prevalence of an agricultural loan program intended to increase the bed net coverage. Farmer households assigned to the program could take advantage of a deferred payment and a discount in the purchase of new bed nets. Our analysis shows a lack of evidence of an effect of the offering of the program to a cluster of households through spillover effects, that is, through a greater bed net coverage in the neighborhood. Supplementary materials for this article are available online.

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  • Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
  • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:514:p:510-525
    DOI: 10.1080/01621459.2015.1125788
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    References listed on IDEAS

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    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    3. 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).
    4. Tyler J. Vanderweele & Guanglei Hong & Stephanie M. Jones & Joshua L. Brown, 2013. "Mediation and Spillover Effects in Group-Randomized Trials: A Case Study of the 4Rs Educational Intervention," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 469-482, June.
    5. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    6. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    7. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    8. Tyler J. VanderWeele, 2010. "Direct and Indirect Effects for Neighborhood-Based Clustered and Longitudinal Data," Sociological Methods & Research, , vol. 38(4), pages 515-544, May.
    9. Bartolucci, Francesco & Grilli, Leonardo, 2011. "Modeling Partial Compliance Through Copulas in a Principal Stratification Framework," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 469-479.
    10. Barnard J. & Frangakis C.E. & Hill J.L. & Rubin D.B., 2003. "Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 299-323, January.
    11. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    12. Jennifer Hill & Jane Waldfogel & Jeanne Brooks-Gunn, 2002. "Differential effects of high-quality child care," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 601-627.
    13. Thomas R. Ten Have & Marshall M. Joffe & Kevin G. Lynch & Gregory K. Brown & Stephen A. Maisto & Aaron T. Beck, 2007. "Causal Mediation Analyses with Rank Preserving Models," Biometrics, The International Biometric Society, vol. 63(3), pages 926-934, September.
    14. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
    15. Mealli, Fabrizia & Rubin, Donald B., 2003. "Assumptions allowing the estimation of direct causal effects," Journal of Econometrics, Elsevier, vol. 112(1), pages 79-87, January.
    16. Jin, Hui & Rubin, Donald B., 2008. "Principal Stratification for Causal Inference With Extended Partial Compliance," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 101-111, March.
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    2. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy," Papers 2005.09130, arXiv.org, revised Jun 2020.
    3. Zhichao Jiang & Kosuke Imai & Anup Malani, 2023. "Statistical inference and power analysis for direct and spillover effects in two‐stage randomized experiments," Biometrics, The International Biometric Society, vol. 79(3), pages 2370-2381, September.
    4. Silvia Noirjean & Marco Mariani & Alessandra Mattei & Fabrizia Mealli, 2020. "Exploiting network information to disentangle spillover effects in a field experiment on teens' museum attendance," Papers 2011.11023, arXiv.org, revised May 2022.
    5. Laura Forastiere & Patrizia Lattarulo & Marco Mariani & Fabrizia Mealli & Laura Razzolini, 2021. "Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 244-258, January.
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    7. Chanmin Kim & Lucas R. F. Henneman & Christine Choirat & Corwin M. Zigler, 2020. "Health effects of power plant emissions through ambient air quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1677-1703, October.

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