Adjusting for confounding in graphs

This section focuses on the sophisticated mechanisms of association and causation within DAGs, employing advanced tools such as d-separation, do-operator, and front and back door adjustments. These approaches are vital for recognizing and mitigating confounding factors. They enable the extraction of actionable insights, and supporting informed decision-making in areas where understanding causal structures is essential. In contexts where controlled experiments are impractical or unethical, these principles provide a solid foundation to infer causal relationships from observational data. We will progressively explore these concepts, focusing on their theoretical foundations and real-world utility.

D-separation

D-separation stands as a cornerstone concept in causal inference and graphical models. It is a structured method to ascertain conditional independencies within a DAG. The essence of understanding d-separation lies in the identification...