  Despite their widespread adoption, neural conversation models have yet to
exhibit natural chat capabilities with humans. In this research, we examine
user utterances as causes and generated responses as effects, recognizing that
changes in a cause should produce a different effect. To further explore this
concept, we have compiled and expanded upon a new dataset called CausalDialogue
through crowd-sourcing. This dataset includes multiple cause-effect pairs
within a directed acyclic graph (DAG) structure. Our analysis reveals that
traditional loss functions struggle to effectively incorporate the DAG
structure, leading us to propose a causality-enhanced method called Exponential
Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at
the utterance level in training neural conversation models. To evaluate the
needs of considering causality in dialogue generation, we built a comprehensive
benchmark on CausalDialogue dataset using different models, inference, and
training methods. Through experiments, we find that a causality-inspired loss
like ExMATE can improve the diversity and agility of conventional loss function
and there is still room for improvement to reach human-level quality on this
new dataset.
