Confounding-Aware Counterfactual Prediction under Global Continuous Treatments

Counterfactual prediction is difficult when treatment is both global and continuous. In settings such as macroeconomic stress testing, all units are exposed to the same treatment path at a given time, eliminating contemporaneous treated–control variation, while latent aggregate factors may jointly affect treatment and outcomes. As a result, observational predictive relationships need not identify responses to external interventions. We develop a confounding-aware framework for counterfactual prediction in this setting. Under a sensitivity model for unobserved aggregate confounding, we derive partially identified sets for counterfactual outcomes under alternative continuous treatment paths, characterizing how causal uncertainty varies with the strength of confounding. We combine these bounds with existing conformal prediction methods to account for finite-sample uncertainty. Experiments on semi-synthetic data and a large-scale stress-testing application show that standard predictive approaches can substantially understate counterfactual uncertainty, while our framework provides informative sensitivity-aware prediction sets.