The quality of requirements specifications may impact subsequent, dependent software engineering (SE) activities. However, empirical evidence of this impact remains scarce and too often superficial as studies abstract from the phenomena under investigation too much. One of these abstractions is caused by frequentist methods which reduce complex data to binary results. In this study, we aim to contrast frequentist methods with more sophisticated Bayesian methods for causal inference. To this end, we reanalyze the only known controlled experiment investigating the impact of passive voice on the subsequent activity of domain modeling. We follow a framework for statistical causal inference and employ Bayesian data analysis methods to re-investigate the hypotheses of the original study. Our results reveal that the effects observed by the original authors relying on frequentist methods turned out to be much less significant than previously assumed. This study supports the recent call to action in SE research to adopt Bayesian data analysis methods for more sophisticated causal inference.