  Causal reasoning is fundamental to human intelligence and crucial for
effective decision-making in real-world environments. Despite recent
advancements in large vision-language models (LVLMs), their ability to
comprehend causality remains unclear. Previous work typically focuses on
commonsense causality between events and/or actions, which is insufficient for
applications like embodied agents and lacks the explicitly defined causal
graphs required for formal causal reasoning. To overcome these limitations, we
introduce a fine-grained and unified definition of causality involving
interactions between humans and/or objects. Building on the definition, we
construct a novel dataset, CELLO, consisting of 14,094 causal questions across
all four levels of causality: discovery, association, intervention, and
counterfactual. This dataset surpasses traditional commonsense causality by
including explicit causal graphs that detail the interactions between humans
and objects. Extensive experiments on CELLO reveal that current LVLMs still
struggle with causal reasoning tasks, but they can benefit significantly from
our proposed CELLO-CoT, a causally inspired chain-of-thought prompting
strategy. Both quantitative and qualitative analyses from this study provide
valuable insights for future research. Our project page is at
https://github.com/OpenCausaLab/CELLO.
