Causal Machine Learning for Financial Risk Attribution under Market Shifts
Keywords:
causal machine learning, financial risk attribution, market regime shifts, model governance, robustness, calibration, fairness, risk management, socio-technical systemsAbstract
Financial risk attribution has traditionally relied on statistical decompositions, factor models, and expert judgment, but these approaches often struggle when market relationships change abruptly. Market shifts alter correlations, liquidity conditions, volatility structures, and the causal pathways through which shocks propagate, making conventional attribution unstable and potentially misleading. This paper examines causal machine learning as a system-level approach for financial risk attribution under market shifts. Rather than treating prediction as the primary objective, causal machine learning seeks to distinguish stable structural relationships from contingent associations, estimate heterogeneous effects, and support counterfactual reasoning about loss drivers. The paper argues that effective adoption requires more than improved algorithms. It demands integrated data infrastructure, model governance, calibration routines, robustness testing, fairness safeguards, and deployment practices that recognize financial institutions as socio-technical systems. We analyze architectural trade-offs across causal discovery, effect estimation, attribution engines, and human oversight. We also consider regulatory expectations, model risk management, sustainability of continuous monitoring, and cross-domain lessons from healthcare, energy, and autonomous systems. The central conclusion is that causal machine learning can improve risk attribution under market shifts only when embedded within a governance and infrastructure framework that treats causal claims as accountable, testable, and revisable. Financial institutions should therefore invest in provenance-rich data, regime-aware validation, calibrated uncertainty, and multi-agent decision workflows that preserve human accountability while exploiting machine-assisted causal inference.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
