IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i4p3126-3139.html
   My bibliography  Save this article

Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints

Author

Listed:
  • Kara E. Rudolph
  • Nicholas Williams
  • Iván Díaz

Abstract

Natural direct and indirect effects are mediational estimands that decompose the average treatment effect and describe how outcomes would be affected by contrasting levels of a treatment through changes induced in mediator values (in the case of the indirect effect) or not through induced changes in the mediator values (in the case of the direct effect). Natural direct and indirect effects are not generally point‐identified in the presence of a treatment‐induced confounder; however, they may be identified if one is willing to assume monotonicity between the treatment and the treatment‐induced confounder. We argue that this assumption may be reasonable in the relatively common encouragement‐design trial setting, where the intervention is randomized treatment assignment and the treatment‐induced confounder is whether or not treatment was actually taken/adhered to. We develop efficiency theory for the natural direct and indirect effects under this monotonicity assumption, and use it to propose a nonparametric, multiply robust estimator. We demonstrate the finite sample properties of this estimator using a simulation study, and apply it to data from the Moving to Opportunity Study to estimate the natural direct and indirect effects of being randomly assigned to receive a Section 8 housing voucher—the most common form of federal housing assistance—on risk developing any mood or externalizing disorder among adolescent boys, possibly operating through various school and community characteristics.

Suggested Citation

  • Kara E. Rudolph & Nicholas Williams & Iván Díaz, 2023. "Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints," Biometrics, The International Biometric Society, vol. 79(4), pages 3126-3139, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3126-3139
    DOI: 10.1111/biom.13850
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13850
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13850?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. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Oliver Dukes & Torben Martinussen & Eric J. Tchetgen Tchetgen & Stijn Vansteelandt, 2019. "On doubly robust estimation of the hazard difference," Biometrics, The International Biometric Society, vol. 75(1), pages 100-109, March.
    3. Kessler, Ronald & Duncan, Greg J. & Gennetian, Lisa A. & Katz, Lawrence F. & Kling, Jeffrey R. & Sampson, Nancy A. & Sanbonmatsu, Lisa & Zaslavsky, Alan M. & Ludwig, Jens, 2014. "Associations of Housing Mobility Interventions for Children in High-Poverty Neighborhoods With Subsequent Mental Disorders During Adolescence," Scholarly Articles 34309063, Harvard University Department of Economics.
    4. van der Laan Mark, 2017. "A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso," The International Journal of Biostatistics, De Gruyter, vol. 13(2), pages 1-35, November.
    5. Jeffrey R Kling & Jeffrey B Liebman & Lawrence F Katz, 2007. "Experimental Analysis of Neighborhood Effects," Econometrica, Econometric Society, vol. 75(1), pages 83-119, January.
    Full references (including those not matched with items on IDEAS)

    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. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    2. Esther Gehrke & Friederike Lenel & Claudia Schupp, 2023. "Occupational Aspirations and Investments in Education: Experimental Evidence from Cambodia," CESifo Working Paper Series 10608, CESifo.
    3. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    4. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    5. Gehrke, Esther & Lenel, Friederike & Schupp, Claudia, 2022. "Interest exploration and investments in education: Experimental evidence from Cambodia," OSF Preprints k6tqr, Center for Open Science.
    6. van Ours, Jan C. & Williams, Jenny & Ward, Shannon, 2015. "Bad Behavior: Delinquency, Arrest and Early School Leaving," CEPR Discussion Papers 10755, C.E.P.R. Discussion Papers.
    7. Gustavo J. Bobonis & Paul J. Gertler & Marco Gonzalez-Navarro & Simeon Nichter, 2022. "Vulnerability and Clientelism," American Economic Review, American Economic Association, vol. 112(11), pages 3627-3659, November.
    8. de Brauw, Alan & Gilligan, Daniel O. & Hoddinott, John & Roy, Shalini, 2014. "The Impact of Bolsa Família on Women’s Decision-Making Power," World Development, Elsevier, vol. 59(C), pages 487-504.
    9. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    11. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    13. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    14. Daron Acemoglu & Amy Finkelstein, 2008. "Input and Technology Choices in Regulated Industries: Evidence from the Health Care Sector," Journal of Political Economy, University of Chicago Press, vol. 116(5), pages 837-880, October.
    15. Rute M. Caeiro & Pedro C. Vicente, 2020. "Knowledge of vitamin A deficiency and crop adoption: Evidence from a field experiment in Mozambique," Agricultural Economics, International Association of Agricultural Economists, vol. 51(2), pages 175-190, March.
    16. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    17. Prina, Silvia, 2015. "Banking the poor via savings accounts: Evidence from a field experiment," Journal of Development Economics, Elsevier, vol. 115(C), pages 16-31.
    18. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    19. Timothy Besley & Rohini Pande & Vijayendra Rao, 2012. "Just Rewards? Local Politics and Public Resource Allocation in South India," The World Bank Economic Review, World Bank, vol. 26(2), pages 191-216.
    20. Lasse J. Jessen & Sebastian Koehne & Patrick Nüß & Jens Ruhose, 2024. "Socioeconomic Inequality in Life Expectancy: Perception and Policy Demand," CESifo Working Paper Series 10940, CESifo.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:79:y:2023:i:4:p:3126-3139. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.