Causal inference studies what would happen under interventions rather than what merely co-moves in observed data. The field connects design, assumptions, and estimation: a randomized trial, a DAG, an IV design, and a weighted estimator are all different ways to argue for the same counterfactual comparison. The core practical question is not whether a model fits well, but whether the identifying assumptions are plausible for the estimand you actually care about. Read the tables as a workflow: define the target effect, map the data-generating process, choose an identification strategy, stress-test the assumptions, and only then optimize estimation.