A Counterfactual Diagnostic Framework for Explaining KS Deterioration in Credit Risk Model Validation

Battle Count: 61

The paper is focused on credit risk model validation and governance, not on trading strategies or market microstructure. However, the underlying statistical concepts (KS statistic, covariate shift detection, importance weighting, bootstrap inference) are transferable to quantitative trading contexts such as alpha model monitoring, regime detection, and factor model validation. The sequential diagnostic logic could inspire similar frameworks for trading model performance attribution.

The framework involves multiple statistical components: stratified bootstrap resampling, weighted empirical CDF computation, product-mix reweighting, domain classifier training for covariate shift detection, and importance weighting. Step 2 requires careful handling of segment definitions and common-support identification. Step 3 requires training a domain classifier and computing density ratios. The sequential gateway logic adds orchestration complexity. However, each individual component uses well-established statistical techniques. The main complexity lies in correctly implementing the weighted KS computation and ensuring proper alignment across steps.

The paper provides detailed mathematical formulations for all four steps, specifies bootstrap parameters (1000 replications, 95% CI), and describes synthetic data generation procedures with explicit configurations (Table 2). However, no code repository is provided, the domain classifier choice in Step 3 is left to the practitioner, and the governance threshold tau is set as a parameter without prescriptive guidance. Simulation parameters are described but full code is not available.