The Bayesian Causal Effect Estimation Algorithm

Journal of Causal Inference

article

Denis Talbot¹  Geneviève Lefebvre²  Juli Atherton² 

[1] Département de médecine sociale et préventive, Université Laval;Département de mathématiques, Université du Québec à Montréal

关键词: model selection;    causal diagrams;    exposure effect estimation;    variance reduction;   

DOI  :  10.1515/jci-2014-0035

来源: De Gruyter

【 摘 要 】

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.

【 授权许可】

CC BY