Cross-site generalizability in medical AI is fundamentally compromised by selection bias, a structural mechanism where patient demographics (e.g., age, severity) non-randomly dictate hospital assignment. Conventional Domain Generalization (DG) paradigms, which predominantly target image-level distribution shifts, fail to address the resulting spurious correlations between site-specific variations and diagnostic labels. To surmount this identifiability barrier, we propose CIV-DG, a causal framework that leverages Conditional Instrumental Variables to disentangle pathological semantics from scanner-induced artifacts. By relaxing the strict random assignment assumption of standard IV methods, CIV-DG accommodates complex clinical scenarios where hospital selection is endogenously driven by patient demographics. We instantiate this theory via a Deep Generalized Method of Moments (DeepGMM) architecture, employing a conditional critic to minimize moment violations and enforce instrument-error orthogonality within demographic strata. Extensive experiments on the Camelyon17 benchmark and large-scale Chest X-Ray datasets demonstrate that CIV-DG significantly outperforms leading baselines, validating the efficacy of conditional causal mechanisms in resolving structural confounding for robust medical AI.

翻译：医学AI的跨机构泛化能力从根本上受到选择偏倚的制约——这是一种结构性机制，其中患者人口统计学特征（如年龄、病情严重程度）非随机地决定了医院的分配。传统领域泛化范式主要针对图像层面的分布偏移，未能解决由此产生的机构特异性变异与诊断标签之间的伪相关。为克服这一可识别性障碍，我们提出CIV-DG这一因果框架，利用条件工具变量将病理语义与扫描仪伪影解耦。通过放宽标准工具变量方法的严格随机分配假设，CIV-DG能够适应医院选择由患者人口统计学特征内生驱动的复杂临床场景。我们通过深度广义矩估计架构实例化该理论，采用条件判别器最小化矩条件偏离，并在人口统计学分层内强制执行工具变量与误差的正交性。在Camelyon17基准测试和大规模胸部X光数据集上的大量实验表明，CIV-DG显著优于主流基线方法，验证了条件因果机制在解决结构性混杂问题以构建鲁棒医学AI中的有效性。