Synthesis: Causal Inference and Bayesian Beliefs

Explain how structural causal models provide a formal language for counterfactual queries (Level 3 of the Causation Ladder).

Describe how Bayesian methods can be used to update beliefs about the structure of a causal model in light of new evidence.

Analyze a case study (e.g., a variant of Simpson's Paradox) to demonstrate how causal and Bayesian reasoning combine to guide decision-making.