Causal Inference: From Correlation to Consequence

Every model whose output drives a decision is answering a causal question, whatever it was trained on. This path builds the machinery that makes those questions answerable: structural causal models and the three ways variables become associated, the identification results that say exactly when observational data suffices and by what formula, the hard limit on learning a graph from data, and where invariance across environments turns causal structure into a training signal.