Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA

• URL: http://arxiv.org/abs/2604.20306v1
• Date: Wed, 22 Apr 2026 08:07:54 GMT
• Title: Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
• Abstract summary: We propose a novel Dual Causal Inference (DCI) framework for MedVQA.<n>DCI integrates Backdoor Adjustment (BDA) and Instrumental Variable (IV) learning to tackle both observable and unobserved confounders.<n>Our method consistently outperforms existing approaches.
• Score: 31.036981541328988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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