  Causal reasoning, the ability to identify cause-and-effect relationship, is
crucial in human thinking. Although large language models (LLMs) succeed in
many NLP tasks, it is still challenging for them to conduct complex causal
reasoning like abductive reasoning and counterfactual reasoning. Given the fact
that programming code may express causal relations more often and explicitly
with conditional statements like ``if``, we want to explore whether Code-LLMs
acquire better causal reasoning abilities. Our experiments show that compared
to text-only LLMs, Code-LLMs with code prompts are significantly better in
causal reasoning. We further intervene on the prompts from different aspects,
and discover that the programming structure is crucial in code prompt design,
while Code-LLMs are robust towards format perturbations.
