  Existing visual question reasoning methods usually fail to explicitly
discover the inherent causal mechanism and ignore jointly modeling cross-modal
event temporality and causality. In this paper, we propose a visual question
reasoning framework named Cross-Modal Question Reasoning (CMQR), to discover
temporal causal structure and mitigate visual spurious correlation by causal
intervention. To explicitly discover visual causal structure, the Visual
Causality Discovery (VCD) architecture is proposed to find question-critical
scene temporally and disentangle the visual spurious correlations by
attention-based front-door causal intervention module named Local-Global Causal
Attention Module (LGCAM). To align the fine-grained interactions between
linguistic semantics and spatial-temporal representations, we build an
Interactive Visual-Linguistic Transformer (IVLT) that builds the multi-modal
co-occurrence interactions between visual and linguistic content. Extensive
experiments on four datasets demonstrate the superiority of CMQR for
discovering visual causal structures and achieving robust question reasoning.
