Causal Machine Learning: A New Paradigm for Interpretable AI Decision-Making

Abstract

Machine learning models have demonstrated remarkable accuracy in predictive tasks, yet they
often operate as black boxes with limited interpretability. Traditional machine learning focuses
on correlation rather than causation, leading to models that can be biased, unreliable, and difficult
to explain. Causal machine learning (CML) introduces a paradigm shift by incorporating causal
inference principles into AI decision-making. This paper explores the integration of causal models
with machine learning techniques to enhance interpretability, robustness, and fairness in AI
systems. We discuss key methodologies, including causal discovery, causal graphs, and
counterfactual reasoning, and present experimental results demonstrating the benefits of CML in
healthcare, finance, and policy-making domains. By prioritizing causality over correlation, CML
paves the way for more accountable and transparent AI models.