IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v3y2015i2p207-236n5.html
   My bibliography  Save this article

The Bayesian Causal Effect Estimation Algorithm

Author

Listed:
  • Talbot Denis

    (Département de médecine sociale et préventive, Université Laval, 1050 avenue de la Médecine, Pavillon Ferdinand-Vandry, Québec, Quebec G1V0A6, Canada Département de mathématiques, Université du Québec à Montréal, 201 avenue du Président-Kennedy PK-5151, Montréal, Quebec H2X 3Y7, Canada)

  • Lefebvre Geneviève

    (Département de mathématiques, Université du Québec à Montréal, 201 avenue du Président-Kennedy PK-5151, Montréal, Quebec H2X 3Y7, Canada)

  • Atherton Juli

    (Département de mathématiques, Université du Québec à Montréal, 201 avenue du Président-Kennedy PK-5151, Montréal, Quebec H2X 3Y7, Canada)

Abstract

Estimating causal exposure effects in observational studies ideally requires the analyst to have a vast knowledge of the domain of application. Investigators often bypass difficulties related to the identification and selection of confounders through the use of fully adjusted outcome regression models. However, since such models likely contain more covariates than required, the variance of the regression coefficient for exposure may be unnecessarily large. Instead of using a fully adjusted model, model selection can be attempted. Most classical statistical model selection approaches, such as Bayesian model averaging, do not readily address causal effect estimation. We present a new model averaged approach to causal inference, Bayesian causal effect estimation (BCEE), which is motivated by the graphical framework for causal inference. BCEE aims to unbiasedly estimate the causal effect of a continuous exposure on a continuous outcome while being more efficient than a fully adjusted approach.

Suggested Citation

  • Talbot Denis & Lefebvre Geneviève & Atherton Juli, 2015. "The Bayesian Causal Effect Estimation Algorithm," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 207-236, September.
    • Handle: RePEc:bpj:causin:v:3:y:2015:i:2:p:207-236:n:5
    DOI: 10.1515/jci-2014-0035
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2014-0035
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2014-0035?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. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 661-671, September.
    2. Chi Wang & Giovanni Parmigiani & Francesca Dominici, 2012. "Rejoinder: Bayesian Effect Estimation Accounting for Adjustment Uncertainty," Biometrics, The International Biometric Society, vol. 68(3), pages 680-686, September.
    3. Ciprian M. Crainiceanu & Francesca Dominici & Giovanni Parmigiani, 2008. "Adjustment uncertainty in effect estimation," Biometrika, Biometrika Trust, vol. 95(3), pages 635-651.
    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. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.
    2. Lefebvre, Geneviève & Atherton, Juli & Talbot, Denis, 2014. "The effect of the prior distribution in the Bayesian Adjustment for Confounding algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 227-240.
    3. Antonelli Joseph & Cefalu Matthew, 2020. "Averaging causal estimators in high dimensions," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 92-107, January.
    4. Ander Wilson & Brian J. Reich, 2014. "Confounder selection via penalized credible regions," Biometrics, The International Biometric Society, vol. 70(4), pages 852-861, December.
    5. M.J. Daniels & C. Wang & B.H. Marcus, 2014. "Fully Bayesian inference under ignorable missingness in the presence of auxiliary covariates," Biometrics, The International Biometric Society, vol. 70(1), pages 62-72, March.
    6. Paola Berchialla & Veronica Sciannameo & Sara Urru & Corrado Lanera & Danila Azzolina & Dario Gregori & Ileana Baldi, 2021. "Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches," IJERPH, MDPI, vol. 18(15), pages 1-9, July.
    7. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    8. Adam A. Szpiro & Lianne Sheppard & Sara D. Adar & Joel D. Kaufman, 2014. "Estimating acute air pollution health effects from cohort study data," Biometrics, The International Biometric Society, vol. 70(1), pages 164-174, March.
    9. Jennifer F. Bobb & Maricela F. Cruz & Stephen J. Mooney & Adam Drewnowski & David Arterburn & Andrea J. Cook, 2022. "Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1271-1293, July.
    10. Chanmin Kim & Mauricio Tec & Corwin Zigler, 2023. "Bayesian nonparametric adjustment of confounding," Biometrics, The International Biometric Society, vol. 79(4), pages 3252-3265, December.
    11. Corwin M. Zigler & Krista Watts & Robert W. Yeh & Yun Wang & Brent A. Coull & Francesca Dominici, 2013. "Model Feedback in Bayesian Propensity Score Estimation," Biometrics, The International Biometric Society, vol. 69(1), pages 263-273, March.
    12. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
    13. R. Gutman & D.B. Rubin, 2012. "Analyses that Inform Policy Decisions," Biometrics, The International Biometric Society, vol. 68(3), pages 671-675, September.
    14. Mark P Little & Alexander G Kukush & Sergii V Masiuk & Sergiy Shklyar & Raymond J Carroll & Jay H Lubin & Deukwoo Kwon & Alina V Brenner & Mykola D Tronko & Kiyohiko Mabuchi & Tetiana I Bogdanova & Ma, 2014. "Impact of Uncertainties in Exposure Assessment on Estimates of Thyroid Cancer Risk among Ukrainian Children and Adolescents Exposed from the Chernobyl Accident," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    15. Ander Wilson & Corwin M. Zigler & Chirag J. Patel & Francesca Dominici, 2018. "Model‐averaged confounder adjustment for estimating multivariate exposure effects with linear regression," Biometrics, The International Biometric Society, vol. 74(3), pages 1034-1044, September.
    16. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    17. Chi Wang & Francesca Dominici & Giovanni Parmigiani & Corwin Matthew Zigler, 2015. "Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models," Biometrics, The International Biometric Society, vol. 71(3), pages 654-665, September.
    18. Brandon Koch & David M. Vock & Julian Wolfson, 2018. "Covariate selection with group lasso and doubly robust estimation of causal effects," Biometrics, The International Biometric Society, vol. 74(1), pages 8-17, March.
    19. Paul Gustafson, 2015. "Discussion of “On Bayesian Estimation of Marginal Structural Models”," Biometrics, The International Biometric Society, vol. 71(2), pages 291-293, June.
    20. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.

    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:bpj:causin:v:3:y:2015:i:2:p:207-236:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.