Causal machine learning combines the tools of machine learning with the rigour of Causal Inference to estimate the effect of interventions, not just predict outcomes. Where standard supervised learning minimises prediction error, causal ML asks how outcomes would change under different actions, which makes it useful for policy evaluation, treatment effect estimation, and Counterfactual Inference.