We are pleased to submit our manuscript, "When LLMs Default to Traffic: Systematic Domain Bias in Causal Mechanism Generation," for consideration in the **Special Issue on Explainable Neurosymbolic AI (X-NeSy)**. This paper introduces CausalAudit, a post-hoc neuro-symbolic verification framework that audits LLM-generated causal explanations against five domain-grounded constraints motivated by principles of causal modeling. Applying CausalAudit across six LLM families and 195 scenarios spanning six application domains, we identify a reproducible failure mode we term the "traffic accident default," in which models systematically generate transportation-centered causal mechanisms for scenarios entirely unrelated to vehicles, including healthcare, finance, and public-safety contexts. This bias persists across a range of prompting interventions, pointing to a distributional rather than instruction-following origin. We validate our primary constraint checker against human annotations, and we discuss the implications for verification-layer approaches to explanation reliability. We believe this work aligns closely with the special issue's focus on causal learning and explanation, formal verification for explainability, and foundations of evaluation for neuro-symbolic systems. We have included our full scenario set and constraint-checker implementations as supplementary material to support reproducibility. This manuscript is original, has not been published elsewhere, and is not under consideration at any other venue. We have no conflicts of interest to disclose. We thank the editors and reviewers for their time and consideration.