The article presents new methods for evaluating bivariate causal statements, particularly in the context of acyclic linear models, aiming to improve the reliability of causal claims made by both human experts and AI. By introducing compatibility and incompatibility scores, the research seeks to address challenges in assessing causal effects where ground truth is elusive.