Causal Inference Is Eating Machine Learning

Traditional machine learning models excel at finding correlations for prediction but often fail when used for decision-making, as they cannot distinguish correlation from causation. This gap, known as confounding, can lead to interventions that worsen outcomes, as seen in examples from healthcare where predictive models failed to identify the true causes of patient readmissions. The content introduces Judea Pearl's "Ladder of Causation" to frame the difference between simple prediction (seeing) and the interventional or counterfactual reasoning (doing/imagining) required for effective business decisions. It concludes by highlighting that modern Python libraries like DoWhy and EconML have made causal inference methods accessible, enabling data scientists to build models that understand cause and effect.