  The use of generative AI to create text descriptions from graphs has mostly
focused on knowledge graphs, which connect concepts using facts. In this work
we explore the capability of large pretrained language models to generate text
from causal graphs, where salient concepts are represented as nodes and
causality is represented via directed, typed edges. The causal reasoning
encoded in these graphs can support applications as diverse as healthcare or
marketing. Using two publicly available causal graph datasets, we empirically
investigate the performance of four GPT-3 models under various settings. Our
results indicate that while causal text descriptions improve with training
data, compared to fact-based graphs, they are harder to generate under
zero-shot settings. Results further suggest that users of generative AI can
deploy future applications faster since similar performances are obtained when
training a model with only a few examples as compared to fine-tuning via a
large curated dataset.
