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The Bayesian Causal Effect Estimation Algorithm

Author

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  • 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
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    References listed on IDEAS

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    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. Ciprian M. Crainiceanu & Francesca Dominici & Giovanni Parmigiani, 2008. "Adjustment uncertainty in effect estimation," Biometrika, Biometrika Trust, vol. 95(3), pages 635-651.
    3. 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.
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