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Identifying direct and indirect effects in a non‐counterfactual framework

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  • Sara Geneletti

Abstract

Summary. Identifying direct and indirect effects is a common problem in the social science and medical literature and can be described as follows. A treatment is administered and a response is recorded. However, another variable mediates the effect of the treatment on the response, in some way channelling a part of the treatment effect. The question is how to extricate the direct and channelled (indirect) effects from one another when it is not possible to intervene on the mediating variable. The aim of the paper is to tackle this problem by using a model for direct and indirect effects based on the decision theoretic framework for causal inference.

Suggested Citation

  • Sara Geneletti, 2007. "Identifying direct and indirect effects in a non‐counterfactual framework," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 199-215, April.
    • Handle: RePEc:bla:jorssb:v:69:y:2007:i:2:p:199-215
    DOI: 10.1111/j.1467-9868.2007.00584.x
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    Cited by:

    1. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    2. Kuha, Jouni & Bukodi, Erzsebet & Goldthorpe, John H, 2019. "Mediation analysis for associations of categorical variables: The role of education in social class mobility in Britain," SocArXiv rm9qy, Center for Open Science.
    3. Marco Doretti & Martina Raggi & Elena Stanghellini, 2022. "Exact parametric causal mediation analysis for a binary outcome with a binary mediator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 87-108, March.
    4. Soojin Park & Kevin M. Esterling, 2021. "Sensitivity Analysis for Pretreatment Confounding With Multiple Mediators," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 85-108, February.
    5. Christian Dippel & Robert Gold & Stephan Heblich & Rodrigo Pinto, 2017. "Instrumental Variables and Causal Mechanisms: Unpacking the Effect of Trade on Workers and Voters," CESifo Working Paper Series 6816, CESifo.
    6. Jouni Kuha & John H. Goldthorpe, 2010. "Path analysis for discrete variables: the role of education in social mobility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 351-369, April.
    7. Jing Huang & Ying Yuan & David Wetter, 2019. "Latent Class Dynamic Mediation Model with Application to Smoking Cessation Data," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 1-18, March.
    8. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
    9. Zhe Chen & Apurbo Sarkar & Md. Shakhawat Hossain & Xiaojing Li & Xianli Xia, 2021. "Household Labour Migration and Farmers’ Access to Productive Agricultural Services: A Case Study from Chinese Provinces," Agriculture, MDPI, vol. 11(10), pages 1-20, October.
    10. Wodtke, Geoffrey & Zhou, Xiang, 2019. "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-residuals Approach," SocArXiv 86d2k, Center for Open Science.
    11. van der Laan Mark J. & Petersen Maya L, 2008. "Direct Effect Models," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-29, October.
    12. Vanessa Didelez, 2019. "Defining causal mediation with a longitudinal mediator and a survival outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 593-610, October.
    13. Kuha, Jouni & Bukodi, Erzsébet & Goldthorpe, John H., 2021. "Mediation analysis for associations of categorical variables: the role of education in social class mobility in Britain," LSE Research Online Documents on Economics 110157, London School of Economics and Political Science, LSE Library.
    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. Daniel Commenges & Anne Gégout‐Petit, 2009. "A general dynamical statistical model with causal interpretation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 719-736, June.
    16. Tyler J. VanderWeele & Eric J. Tchetgen Tchetgen, 2017. "Mediation analysis with time varying exposures and mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 917-938, June.
    17. Soojin Park & Peter M. Steiner & David Kaplan, 2018. "Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 298-320, June.
    18. Timothy Gage & Fu Fang & Erin O’Neill & Greg DiRienzo, 2013. "Maternal Education, Birth Weight, and Infant Mortality in the United States," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 615-635, April.

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