  Our investigation into the Affective Reasoning in Conversation (ARC) task
highlights the challenge of causal discrimination. Almost all existing models,
including large language models (LLMs), excel at capturing semantic
correlations within utterance embeddings but fall short in determining the
specific causal relationships. To overcome this limitation, we propose the
incorporation of \textit{i.i.d.} noise terms into the conversation process,
thereby constructing a structural causal model (SCM). It explores how distinct
causal relationships of fitted embeddings can be discerned through independent
conditions. To facilitate the implementation of deep learning, we introduce the
cogn frameworks to handle unstructured conversation data, and employ an
autoencoder architecture to regard the unobservable noise as learnable
"implicit causes." Moreover, we curate a synthetic dataset that includes i.i.d.
noise. Through comprehensive experiments, we validate the effectiveness and
interpretability of our approach. Our code is available in
https://github.com/Zodiark-ch/mater-of-our-EMNLP2023-paper.
