Why Your Best Predictive Model Gives the Wrong Treatment Effect

Your best predictive model might be the worst choice for understanding a treatment's causal effect, as prediction and causal estimation are fundamentally different goals. This failure happens when a crucial confounder is dropped for having low predictive power on the outcome, even though its strong correlation with the treatment contaminates the final effect