From Control to Treatment: Causal Forecasting Beyond the Observed Panel

Conventional panel data methods recover retrospective counterfactuals within the observed horizon. Many policy decisions, however, are prospective: how would an untreated unit evolve beyond the observed panel under an intervention it has not previously experienced? We develop a causal forecasting framework that combines the counterfactual logic of synthetic controls with multivariate time-series extrapolation. Under a latent factor model, we impose low-rank structure on the treated-state time factors. This yields the Two-Way Synthetic Forecasting estimator, which learns cross-unit weights from pre-treatment outcomes and temporal dynamics from treated donors' post-treatment trajectories. We establish pointwise identification, finite-sample error bounds, consistency, asymptotic normality, and inference for prespecified linear summaries over fixed forecast horizons. Simulations support the theory, and an application to the opening of NFL stadiums during the 2020 season illustrates how the method can inform a prospective policy change using only information available at the decision date.