Causal Inference

Core principle: Correlation is not causation — but sometimes it is, and knowing which matters enormously. Use counterfactuals, confounders, and causal structure to ask "did X actually cause Y?" rigorously before acting on data.

The Core Distinction

Correlation: X and Y move together. Causation: Changing X changes Y — and we know why.

Why it matters:

• Intervening on a correlate with no causal path wastes effort
• Missing a confounder leads to attributing effects to the wrong cause
• Acting on spurious correlation can make things worse

Key Concepts