  Context-Aware Emotion Recognition (CAER) is a crucial and challenging task
that aims to perceive the emotional states of the target person with contextual
information. Recent approaches invariably focus on designing sophisticated
architectures or mechanisms to extract seemingly meaningful representations
from subjects and contexts. However, a long-overlooked issue is that a context
bias in existing datasets leads to a significantly unbalanced distribution of
emotional states among different context scenarios. Concretely, the harmful
bias is a confounder that misleads existing models to learn spurious
correlations based on conventional likelihood estimation, significantly
limiting the models' performance. To tackle the issue, this paper provides a
causality-based perspective to disentangle the models from the impact of such
bias, and formulate the causalities among variables in the CAER task via a
tailored causal graph. Then, we propose a Contextual Causal Intervention Module
(CCIM) based on the backdoor adjustment to de-confound the confounder and
exploit the true causal effect for model training. CCIM is plug-in and
model-agnostic, which improves diverse state-of-the-art approaches by
considerable margins. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our CCIM and the significance of causal
insight.
