In causal reasoning, a counterfactual problem asks “what if?”—specifically, what would have happened had a different action or condition occurred, given the same context. This analysis powers AI fairness audits, policy evaluation, and medical treatment decisions. Data scientists, economists, and ethicists benefit by testing hypothetical scenarios to measure true causal impact, uncover bias, and optimize interventions without costly real-world experiments.
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