Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction

ArXi:2603.07438v1 Announce Type: new Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios -- a fundamentally causal question typically treated as a prediction problem. We propose a framework for policy-path counterfactual inference in panels that transparently separates what can be learned from data from what requires assumptions about confounding.