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Directed Acyclic Graphs as a causal inference tool: from principles to applications
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Epidemiologic studies often address causal questions, which require considering confounding issues. Identifying and adjusting for confounders is challenging and various methods have been proposed. Directed acyclic graphs (DAGs) are a causal inference tool, being simultaneously qualitative and quantitative. They offer us a representation of the relationship between variables and support the selection of confounders to consider. In this talk, main issues with previous approaches will be presented, followed by an overview of DAGs, alongside with its main applications and developments.
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Directed Acyclic Graphs Exposure on Disease Grafos Acíclicos Dirigidos Determinantes da Saúde e da Doença Epidemiologia