C3E-RRG:Confounder-Aware Causal Evidence Coupling and Evolution for Chest X-Ray Report Generation

Chest X-ray report generation aims to produce diagnostic descriptions grounded in imaging evidence. However, correlation-driven models may exploit reporting templates and disease co-occurrence patterns, leading to unsupported findings and semantic drift. To address this issue, we propose C3E-RRG, a confounder-aware causal debiasing framework for chest X-ray report generation that incorporates explicit causal modeling into the encoder-decoder pipeline. Specifically, we first construct confounding variables by combining language priors derived from pointwise mutual information with disease co-occurrence statistics, and apply confounder-aware adjustment to suppress spurious correlations induced by data bias. We then design an evidence-consistent cross-modal coupling module to strengthen the correspondence between visual features and diagnostic semantics, thereby reducing semantic drift caused by high-frequency reporting templates. Finally, we develop a dynamic bidirectional causal evolution mechanism that iteratively revisits visual evidence and refines semantic representations, progressively correcting semantic deviations and improving image-text consistency during long-sequence generation. Experiments on the IU-XRay and MIMIC-CXR datasets demonstrate competitive performance and validate the effectiveness of the proposed components in suppressing confounding effects, aligning visual evidence with diagnostic semantics, and dynamically correcting generation errors. The source code is available at https://github.com/ cloneiq/C3E-RRG.