双重因果推断：整合后门调整与工具变量学习用于医疗视觉问答
Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
AI总结：
本文提出双重因果推断框架，通过整合后门调整和工具变量学习，解决医疗视觉问答中多模态数据中的内在偏见问题，提升模型的诊断推理可靠性。
摘要：
医疗视觉问答（MedVQA）旨在根据复杂的医学图像和问题生成临床可靠的答案。然而，现有方法常过度拟合表面跨模态相关性，忽视多模态医学数据中的内在偏见。因此，模型容易受到跨模态混淆效应的影响，严重阻碍其提供可信诊断推理的能力。为了解决这一限制，我们提出了一种新的双重因果推断（DCI）框架用于MedVQA。到目前为止，DCI是首个统一的架构，整合了后门调整（BDA）和工具变量（IV）学习，共同解决可观察和不可观察的混杂因素。具体而言，我们构建了一个结构因果模型（SCM），其中可观察的跨模态偏见（例如频繁的视觉和文本共现）通过BDA得到缓解，而不可观测的混杂因素则通过从共享潜在空间中学习的IV进行补偿。为了保证IV的有效性，我们设计了互信息约束，最大化其对融合多模态表示的依赖性，同时最小化其与不可观测混杂因素和目标答案的相关性。通过这种双重机制，DCI提取了去混淆的表示，捕捉真正的因果关系。在四个基准数据集SLAKE、SLAKE-CP、VQA-RAD和PathVQA上的大量实验表明，我们的方法在四个基准数据集上表现优异，特别是在分布外（OOD）泛化方面。此外，定性分析证实，DCI通过显式分离真正的因果效应和虚假的跨模态捷径，显著增强了跨模态推理的可解释性和鲁棒性。
英文摘要：
Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However, existing methods often overfit to superficial cross-modal correlations, neglecting the intrinsic biases embedded in multimodal medical data. Consequently, models become vulnerable to cross-modal confounding effects, severely hindering their ability to provide trustworthy diagnostic reasoning. To address this limitation, we propose a novel Dual Causal Inference (DCI) framework for MedVQA. To the best of our knowledge, DCI is the first unified architecture that integrates Backdoor Adjustment (BDA) and Instrumental Variable (IV) learning to jointly tackle both observable and unobserved confounders. Specifically, we formulate a Structural Causal Model (SCM) where observable cross-modal biases (e.g., frequent visual and textual co-occurrences) are mitigated via BDA, while unobserved confounders are compensated using an IV learned from a shared latent space. To guarantee the validity of the IV, we design mutual information constraints that maximize its dependence on the fused multimodal representations while minimizing its associations with the unobserved confounders and target answers. Through this dual mechanism, DCI extracts deconfounded representations that capture genuine causal relationships. Extensive experiments on four benchmark datasets, SLAKE, SLAKE-CP, VQA-RAD, and PathVQA, demonstrate that our method consistently outperforms existing approaches, particularly in out-of-distribution (OOD) generalization. Furthermore, qualitative analyses confirm that DCI significantly enhances the interpretability and robustness of cross-modal reasoning by explicitly disentangling true causal effects from spurious cross-modal shortcuts.
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