Abstract: Graph-based Multiple Instance Learning (MIL) is commonly applied in survival analysis using Hematoxylin and Eosin (H\&E)-stained whole slide images (WSIs) because it effectively captures topological information. However, variations in WSI preparation—such as differences in staining and scanning—can introduce semantic bias. Additionally, topological subgraphs that are not relevant to the causal relationships can create noise, resulting in biased slide-level representations. These issues can hinder both the interpretability and generalization of the analysis. To address these issues, we introduce a dual structural causal model as the theoretical foundation and further propose a novel and interpretable dual causal graph-based MIL model, named C$^2$MIL, for robust survival analysis. C$^2$MIL adopts a novel cross-scale adaptive feature disentangling module for semantic causal intervention and a new Bernoulli differentiable causal subgraph sampling method for topological causal discovery. A joint optimization strategy integrating disentangling supervision and contrastive learning is proposed to ensure simultaneous refinement of semantic and topological causalities. Experimental results reveal that C$^2$MIL outperforms existing methods in both generalization and interpretability and can serve as a causal enhancement for various MIL baselines. The code will be available later.