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

• URL: http://arxiv.org/abs/2603.07438v1
• Date: Sun, 08 Mar 2026 03:22:38 GMT
• Title: Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction
• Abstract summary: Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios.<n>We propose a framework for policy-path counterfactual inference in panels.<n>We validate all results through simulation and semi-synthetic experiments with real unemployment data.
• Score: 2.289837306672451
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. Our approach has four components: (i) observational identification of path-conditional means via iterated regression, enabling continuous macro-path contrasts without requiring a control group; (ii) causal set identification under bounded confounding, yielding sharp identified sets with interpretable breakdown values that communicate robustness in a single number; (iii) an oracle inequality showing that recursive rollout error is governed by a horizon-dependent amplification factor, providing a concrete answer to how far ahead one can reliably predict under stress; and (iv) importance-weighted conformal calibration bands with diagnostics that quantify extrapolation cost and trigger abstention when coverage guarantees degrade. The final output is a three-layer uncertainty decomposition that cleanly separates estimation uncertainty from confounding uncertainty. We validate all results through simulation and semi-synthetic experiments with real unemployment data, including a Covid retrospective demonstrating the framework's diagnostic value under extreme scenarios.

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