  Reasoning, a crucial aspect of NLP research, has not been adequately
addressed by prevailing models including Large Language Model. Conversation
reasoning, as a critical component of it, remains largely unexplored due to the
absence of a well-designed cognitive model. In this paper, inspired by
intuition theory on conversation cognition, we develop a conversation cognitive
model (CCM) that explains how each utterance receives and activates channels of
information recursively. Besides, we algebraically transformed CCM into a
structural causal model (SCM) under some mild assumptions, rendering it
compatible with various causal discovery methods. We further propose a
probabilistic implementation of the SCM for utterance-level relation reasoning.
By leveraging variational inference, it explores substitutes for implicit
causes, addresses the issue of their unobservability, and reconstructs the
causal representations of utterances through the evidence lower bounds.
Moreover, we constructed synthetic and simulated datasets incorporating
implicit causes and complete cause labels, alleviating the current situation
where all available datasets are implicit-causes-agnostic. Extensive
experiments demonstrate that our proposed method significantly outperforms
existing methods on synthetic, simulated, and real-world datasets. Finally, we
analyze the performance of CCM under latent confounders and propose theoretical
ideas for addressing this currently unresolved issue.
