  Recent approaches to empathetic response generation try to incorporate
commonsense knowledge or reasoning about the causes of emotions to better
understand the user's experiences and feelings. However, these approaches
mainly focus on understanding the causalities of context from the user's
perspective, ignoring the system's perspective. In this paper, we propose a
commonsense-based causality explanation approach for diverse empathetic
response generation that considers both the user's perspective (user's desires
and reactions) and the system's perspective (system's intentions and
reactions). We enhance ChatGPT's ability to reason for the system's perspective
by integrating in-context learning with commonsense knowledge. Then, we
integrate the commonsense-based causality explanation with both ChatGPT and a
T5-based model. Experimental evaluations demonstrate that our method
outperforms other comparable methods on both automatic and human evaluations.
