DoWhy — Causal Inference

DoWhy answers the question every analyst actually wants answered: "Does X cause Y, or is it just correlated?" Correlation is everywhere. Causation requires structure — a causal graph that encodes which variables influence which. DoWhy's workflow is three steps: Identify (is the effect estimable from this graph?) → Estimate (compute the effect) → Refute (is this estimate robust?).

Core Mental Model

CORRELATION:  X and Y move together. Could be:
                X → Y          (X causes Y)
                Y → X          (Y causes X)
                X ← C → Y     (C confounds both — spurious!)

CAUSATION:    We need to know WHY they move together.
              A causal graph (DAG) encodes our assumptions.
              Then math tells us: "Given this graph,
              CAN we estimate the causal effect from data?"
              → If yes: which variables to control for?
              → If no: what additional data do we need?