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gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula

Author

Listed:
  • Rhian M. Daniel

    (London School of Hygiene and Tropical Medicine)

  • Bianca L. De Stavola

    (London School of Hygiene and Tropical Medicine)

  • Simon N. Cousens

    (London School of Hygiene and Tropical Medicine)

Abstract

This article describes a new command, gformula, that is an implementation of the g-computation procedure. It is used to estimate the causal effect of time-varying exposures on an outcome in the presence of time-varying confounders that are themselves also affected by the exposures. The procedure also addresses the related problem of estimating direct and indirect effects when the causal ef- fect of the exposures on an outcome is mediated by intermediate variables, and in particular when confounders of the mediator–outcome relationships are them- selves affected by the exposures. A brief overview of the theory and a description of the command and its options are given, and illustrations using two simulated examples are provided. Copyright 2011 by StataCorp LP.

Suggested Citation

  • Rhian M. Daniel & Bianca L. De Stavola & Simon N. Cousens, 2011. "gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula," Stata Journal, StataCorp LLC, vol. 11(4), pages 479-517, December.
    • Handle: RePEc:tsj:stataj:v:11:y:2011:i:4:p:479-517
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    References listed on IDEAS

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    1. Alan E. Hubbard & Mark J. van der Laan, 2008. "Population intervention models in causal inference," Biometrika, Biometrika Trust, vol. 95(1), pages 35-47.
    2. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769, December.
    4. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, IZA Network @ LISER.
    5. Jonathan A. C. Sterne & Kate Tilling, 2002. "G-estimation of causal effects, allowing for time-varying confounding," Stata Journal, StataCorp LLC, vol. 2(2), pages 164-182, May.
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    Cited by:

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    2. Sean Yiu & Qing Wang & Francois Mercier & Frank Dahlke & Fabian Model, 2025. "Application of the Parametric g-Formula to Characterizing Counterfactual Time-to-Event Disability Progression Outcomes in Multiple Sclerosis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(2), pages 501-527, July.
    3. Antonio R. Linero, 2022. "Simulation‐based estimators of analytically intractable causal effects," Biometrics, The International Biometric Society, vol. 78(3), pages 1001-1017, September.
    4. Sourabh Balgi & Adel Daoud & Jose M. Pe~na & Geoffrey T. Wodtke & Jesse Zhou, 2024. "Deep Learning With DAGs," Papers 2401.06864, arXiv.org.
    5. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    6. Delis, Manthos D. & Dioikitopoulos, Evangelos V. & Ongena, Steven, 2023. "Population diversity and financial risk-taking," Journal of Banking & Finance, Elsevier, vol. 151(C).
    7. Berna Beyhan & Derya Fındık, 2022. "Selection of Sustainability Startups for Acceleration: How Prior Access to Financing and Team Features Influence Accelerators’ Selection Decisions," Sustainability, MDPI, vol. 14(4), pages 1-23, February.
    8. 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.
    9. Ploubidis, George B. & Benova, Lenka & Grundy, Emily & Laydon, Daniel & DeStavola, Bianca, 2014. "Lifelong Socio Economic Position and biomarkers of later life health: Testing the contribution of competing hypotheses," Social Science & Medicine, Elsevier, vol. 119(C), pages 258-265.
    10. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    11. Richard J Silverwood & Lee Williamson & Emily M Grundy & Bianca L De Stavola, 2016. "Pathways between Socioeconomic Disadvantage and Childhood Growth in the Scottish Longitudinal Study, 1991–2001," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.
    12. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    13. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2016. "Tackling non-ignorable dropout in the presence of time varying confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 775-795, November.

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