Probabilistic Risk Assessment (PRA) provides a well-established framework for evaluating risk in safety-critical systems by modeling accident scenarios, estimating their likelihood, and assessing potential consequences. Central to PRA is the representation of causal relationships among system components, human actions, and external factors, typically captured through structured logical models and expert judgment. Traditional PRA approaches are largely static, limiting their ability to support dynamic, risk-informed decision-making in evolving systems.

Causal Machine Learning (Causal ML) offers a complementary paradigm by enabling formal intervention and counterfactual analysis. While not inherently dynamic, it provides mechanisms to incorporate time-dependent relationships and update models as new data become available, supporting more adaptive risk assessment. This paper explores the potential integration of causal ML within the PRA framework.

Opportunities include improved modeling of complex dependencies, enhanced capability to evaluate the impact of operational or design changes, and the incorporation of data-driven insights into risk assessments. At the same time, significant challenges remain, including data limitations in safety-critical domains, the need for strong causal assumptions, issues of interpretability, and alignment with established PRA practices.

A conceptual framework is proposed in which causal ML augments traditional PRA by supporting causal structure refinement and decision analysis while maintaining the central role of expert judgment. The paper concludes by outlining research directions for integrating causal methods into safety assessment workflows, emphasizing the importance of combining data-driven approaches with domain expertise to ensure reliability and trust in high-consequence applications.