This paper provides a review of the Causal Alpha Factor Method (CAFM), an approach for quantifying common cause failure (CCF) parameters in probabilistic risk assessments (PRAs) and probabilistic safety assessments (PSAs). CAFM aims to enhance the accuracy of risk modeling by explicitly incorporating failure causes into CCF analysis, thereby enabling more informed interpretations of operational events and supporting risk-informed decision-making. Building upon the traditional alpha factor framework, CAFM introduces cause-specific grouping, gamma weighting, and normalization techniques to reconcile cause-based estimates with generic CCF parameters. The paper reviews the method’s development, the supporting industry data, and key challenges, including assigning single causes to complex events, extrapolating across varying component group sizes, and managing broad uncertainty distributions. This paper concludes with recommendations to improve dataset quality, refine event coding practices, and revise prior distributions to better reflect current industry performance and enhance the reliability of risk models.