Towards explainable visual question answering via cross-modal causal reasoning

Explainable Visual Question Answering (EVQA) aims to not only predict accurate answers to visual questions but also generate human-friendly multimodal explanations that reveal the underlying reasoning process. Despite significant progress, existing EVQA methods suffer from two critical limitations: (1) they often rely on spurious cross-modal correlations (e.g., linguistic biases or visual shortcuts) rather than genuine causal relations, leading to unreliable reasoning; (2) the consistency between predicted answers and generated explanations is compromised due to the lack of explicit modeling of their causal dependencies. To address these issues, we propose a Cross-Modal Causal Reasoning (CMCR) framework that integrates causal inference with multimodal learning to disentangle causal effects from spurious correlations and enforce answer-explanation consistency. Specifically, CMCR incorporates three key innovations: (1) Causal Intervention, which employs backdoor adjustment to eliminate linguistic biases and frontdoor adjustment to mitigate visual shortcut biases; (2) a Neural-Symbolic Explanation Generator designed to translate symbolic reasoning processes into natural language explanations, thereby enhancing process explainability; and (3) Variational Causal Inference, which enforces causal consistency between answers and explanations. Experiments on benchmark datasets demonstrate that CMCR outperforms state-of-the-art methods, achieving a 1.19% higher accuracy, a 1.05% higher grounding for explanation quality, and a 0.42% higher answer-explanation consistency. • We formalize EVQA within a structural causal model to distinguish genuine causal paths. • We design a causal intervention module to refine visual and linguistic representations. • We formulate variational causal inference to enforce the causal consistency. • We introduce a neural-symbolic explanation generator to generate multimodal explanation. • Experiments demonstrate that CMCR outperforms state-of-the-art methods.