Why Causal Inference Matters in Machine Learning and Its Banking Applications

Introduction: This article explains why causal relationships should be incorporated into machine learning and explores applications of causal‑based ML in the banking industry.

1. Necessity of Causality: Traditional ML relies on statistical correlations, which can lead to mismatched training and deployment data and spurious conclusions, illustrated by examples such as the unrelated correlation between Nicolas Cage movies and drowning deaths.

2. Causal Science and Machine Learning: Historical development from Neyman's potential outcomes to Pearl's causal networks, and how these concepts have been integrated into modern ML for interpretability, transferability, robustness, fairness, and counterfactual evaluation.

3. Banking Applications:

Marketing – uplift modeling to identify customers whose response improves most from a treatment, with methods such as direct‑evaluation (S‑learner), transformed‑outcome, and tree‑based split criteria.

Risk Control – causal‑regularized stable learning for credit scoring to improve model stability across time and mitigate performance degradation.

4. Practical Experiments: Synthetic data and real loan‑application datasets demonstrate that causal‑aware rule extraction and stable learning can increase uplift and reduce performance degradation over time.

5. Challenges: Identifying reliable causal features, aligning modeling objectives with business goals, and balancing model effectiveness with stability.

Conclusion: Incorporating causal inference into machine learning offers powerful tools for banking marketing and risk management, though it introduces challenges that require careful methodological design.