  Humans have an innate drive to seek out causality. Whether fuelled by
curiosity or specific goals, we constantly question why things happen, how they
are interconnected, and many other related phenomena. To develop AI agents
capable of addressing this natural human quest for causality, we urgently need
a comprehensive dataset of natural causal questions. Unfortunately, existing
datasets either contain only artificially-crafted questions that do not reflect
real AI usage scenarios or have limited coverage of questions from specific
sources. To address this gap, we present CausalQuest, a dataset of 13,500
naturally occurring questions sourced from social networks, search engines, and
AI assistants. We formalize the definition of causal questions and establish a
taxonomy for finer-grained classification. Through a combined effort of human
annotators and large language models (LLMs), we carefully label the dataset. We
find that 42% of the questions humans ask are indeed causal, with the majority
seeking to understand the causes behind given effects. Using this dataset, we
train efficient classifiers (up to 2.85B parameters) for the binary task of
identifying causal questions, achieving high performance with F1 scores of up
to 0.877. We conclude with a rich set of future research directions that can
build upon our data and models.
