A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures
Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the nee...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-11-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2021-0023 |
| _version_ | 1798014091003953152 |
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| author | Talbot Denis Beaudoin Claudia |
| author_facet | Talbot Denis Beaudoin Claudia |
| author_sort | Talbot Denis |
| collection | DOAJ |
| description | Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of analysts. We propose a generalized Bayesian causal effect estimation (GBCEE) algorithm to perform variable selection and produce double robust (DR) estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution that targets the selection of true confounders and predictors of the outcome for the unbiased estimation of causal effects with reduced standard errors. The Bayesian machinery allows GBCEE to directly produce inferences for its estimate. In simulations, GBCEE was observed to perform similarly or to outperform DR alternatives. Its ability to directly produce inferences is also an important advantage from a computational perspective. The method is finally illustrated for the estimation of the effect of meeting physical activity recommendations on the risk of hip or upper-leg fractures among older women in the study of osteoporotic fractures. The 95% confidence interval produced by GBCEE is 61% narrower than that of a DR estimator adjusting for all potential confounders in this illustration. |
| first_indexed | 2024-04-11T15:11:59Z |
| format | Article |
| id | doaj.art-a571bce4e06846d59e4cb8eb063a45a6 |
| institution | Directory Open Access Journal |
| issn | 2193-3685 |
| language | English |
| last_indexed | 2024-04-11T15:11:59Z |
| publishDate | 2022-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-a571bce4e06846d59e4cb8eb063a45a62022-12-22T04:16:37ZengDe GruyterJournal of Causal Inference2193-36852022-11-0110133537110.1515/jci-2021-0023A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fracturesTalbot Denis0Beaudoin Claudia1Département de médecine sociale et préventive, Université Laval, Québec, CanadaCentre de recherche du CHU de Québec – Université Laval, Québec, CanadaAnalysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of analysts. We propose a generalized Bayesian causal effect estimation (GBCEE) algorithm to perform variable selection and produce double robust (DR) estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution that targets the selection of true confounders and predictors of the outcome for the unbiased estimation of causal effects with reduced standard errors. The Bayesian machinery allows GBCEE to directly produce inferences for its estimate. In simulations, GBCEE was observed to perform similarly or to outperform DR alternatives. Its ability to directly produce inferences is also an important advantage from a computational perspective. The method is finally illustrated for the estimation of the effect of meeting physical activity recommendations on the risk of hip or upper-leg fractures among older women in the study of osteoporotic fractures. The 95% confidence interval produced by GBCEE is 61% narrower than that of a DR estimator adjusting for all potential confounders in this illustration.https://doi.org/10.1515/jci-2021-0023causal inferenceconfoundingdouble robustnessmodel averagingmodel selection62f1562g35 |
| spellingShingle | Talbot Denis Beaudoin Claudia A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures Journal of Causal Inference causal inference confounding double robustness model averaging model selection 62f15 62g35 |
| title | A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| title_full | A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| title_fullStr | A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| title_full_unstemmed | A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| title_short | A generalized double robust Bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| title_sort | generalized double robust bayesian model averaging approach to causal effect estimation with application to the study of osteoporotic fractures |
| topic | causal inference confounding double robustness model averaging model selection 62f15 62g35 |
| url | https://doi.org/10.1515/jci-2021-0023 |
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