How Wrong Can Your Counterfactual Be? Quantifying Confounding Bias for Continuous Treatments without a Control Group

In three linesCausal inference framework for financial stress testing in panel data with continuous treatment and no control group. Proposes closed-form confounding envelope parameterized by two sensitivity parameters, combines partial identification with importance-weighted conformal prediction. Shows standard predictive models remain causally biased on US unemployment data.

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