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Causal AI for Financial Decision-Making Under Uncertainty
Subject area: Science,Engineering and Technology · Area of research: Causal AI for Financial Decision-Making
DOI: https://doi.org/10.64388/IREV5I5-1713472
Abstract
This paper investigates the role of Causal Artificial Intelligence (Causal AI) as a foundation for financial decision-making under uncertainty. While machine learning and statistical models have become central to modern finance, their reliance on correlation-based inference limits their reliability in non-stationary environments characterized by regime shifts, policy interventions, and strategic feedback effects. Financial decision-making inherently involves actions that modify the data-generating process, rendering purely predictive approaches insufficient for robust decision support. Building on structural causal models, counterfactual reasoning, and econometric identification principles, we develop a unified framework that integrates causal inference with modern AI systems for financial applications. The proposed approach is evaluated using controlled simulation environments and empirically inspired scenarios with known structural properties, enabling rigorous assessment of decision quality under distributional change. Across multiple financial settings, including credit allocation, portfolio management, and algorithmic trading, causal decision-making systems exhibit lower decision regret, improved robustness to regime shifts, and greater interpretability compared to correlation-based methods. These results suggest that causal reasoning provides a principled and practically relevant foundation for financial decision-making under uncertainty. While the empirical evaluation is simulation-based, the framework highlights the potential of Causal AI to improve reliability, transparency, and robustness in financial systems operating in dynamic and uncertain environments.
Keywords
Causal AI, Financial Decision-Making, Structural Causal Models, Uncertainty, Regime Shifts, Explainable AI, Risk Management
How to cite this paper
@article{1713472,
author = {Uju Eziokwu},
title = {Causal AI for Financial Decision-Making Under Uncertainty},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {5},
pages = {544-561},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713472.pdf},
abstract = {This paper investigates the role of Causal Artificial Intelligence (Causal AI) as a foundation for financial decision-making under uncertainty. While machine learning and statistical models have become central to modern finance, their reliance on correlation-based inference limits their reliability in non-stationary environments characterized by regime shifts, policy interventions, and strategic feedback effects. Financial decision-making inherently involves actions that modify the data-generating process, rendering purely predictive approaches insufficient for robust decision support. Building on structural causal models, counterfactual reasoning, and econometric identification principles, we develop a unified framework that integrates causal inference with modern AI systems for financial applications. The proposed approach is evaluated using controlled simulation environments and empirically inspired scenarios with known structural properties, enabling rigorous assessment of decision quality under distributional change. Across multiple financial settings, including credit allocation, portfolio management, and algorithmic trading, causal decision-making systems exhibit lower decision regret, improved robustness to regime shifts, and greater interpretability compared to correlation-based methods. These results suggest that causal reasoning provides a principled and practically relevant foundation for financial decision-making under uncertainty. While the empirical evaluation is simulation-based, the framework highlights the potential of Causal AI to improve reliability, transparency, and robustness in financial systems operating in dynamic and uncertain environments.},
keywords = {Causal AI, Financial Decision-Making, Structural Causal Models, Uncertainty, Regime Shifts, Explainable AI, Risk Management},
month = {November},
doi = {https://doi.org/10.64388/IREV5I5-1713472}
}