La moyenne bayésienne pour les modèles basés sur les graphes acycliques orientés
|Authors:||Bouzite, Fatima Ezzahraa|
|Advisor:||Talbot, Denis; Lefebvre, Genevieve|
|Abstract:||Causal inference methods are useful for answering several research questions in different fields, including epidemiology. Directed acyclic graphs are important tools for causal inference.Among other things, they can be used to identify confounding variables used in fitting statistical models to unbiasedly estimate the effect of a treatment. These graphs are built from the knowledge of the domain of application. However, this knowledge is sometimes insufficient to assume that the constructed graph is correct. Often, a researcher can propose various graphs corresponding to the same problem. In this project, we develop an alternative to the traditional Bayesian model averaging which is based on a set of graphs proposed by a user. For its implementation, we f irst estimate the likelihood of the data under the models implied by each graph to determine the posterior probability of each graph. A set of adjustment covariates sufficient to control for confounding bias is identified for each graph and the causal effect is estimated using appropriate approaches by adjusting for these covariates. Finally, the overall causal effect is estimated as a weighted average of the graphspecific estimates. The performance of this approach is studied using a simulation study in which the data generation mechanism is inspired by the Study of Osteoporotic Fractures (SOF). Different scenarios varying in their relationships between the variables are presented. ivThe simulation study shows a good overall performance of our method compared to the traditional Bayesian model averaging. The application of this approach is illustrated using data from the SOF, whose objective is to estimate the effect of physical activity on the risk of hip fractures.|
|Document Type:||Mémoire de maîtrise|
|Open Access Date:||1 November 2021|
|Collection:||Thèses et mémoires|
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