  The chest X-ray (CXR) is commonly employed to diagnose thoracic illnesses,
but the challenge of achieving accurate automatic diagnosis through this method
persists due to the complex relationship between pathology. In recent years,
various deep learning-based approaches have been suggested to tackle this
problem but confounding factors such as image resolution or noise problems
often damage model performance. In this paper, we focus on the chest X-ray
classification task and proposed an interpretable instrumental variable (IV)
learning framework, to eliminate the spurious association and obtain accurate
causal representation. Specifically, we first construct a structural causal
model (SCM) for our task and learn the confounders and the preliminary
representations of IV, we then leverage electronic health record (EHR) as
auxiliary information and we fuse the above feature with our transformer-based
semantic fusion module, so the IV has the medical semantic. Meanwhile, the
reliability of IV is further guaranteed via the constraints of mutual
information between related causal variables. Finally, our approach's
performance is demonstrated using the MIMIC-CXR, NIH ChestX-ray 14, and
CheXpert datasets, and we achieve competitive results.
