Network anomaly detection in the Internet of Vehicles (IoV) struggles with non-IID data caused by dynamic environments, hardware heterogeneity, and communication noise. Traditional methods relying on feature-label correlations suffer from noise-induced spurious associations and performance degradation under distribution shifts. We propose CDS-IoV-NAD, a causal deep stable learning framework with two innovations: A dynamic bit-width binary encoding (DBBE) mechanism that losslessly maps continuous features to {0,1} space through robust Z-score normalization and differentiable encoding, enabling causal intervention compatibility. A dual-regularized causal weight learning model that amplifies causal features via propensity score weighting while suppressing non-causal pseudo-correlations using L2 and stability regularization. Experiments on CIC-IoV24 and UNSW-NB15 show CDS-IoV-NAD achieves > 5% average stability gain under noise interference, with minimal accuracy fluctuation (15% max variance) across distribution shifts. The method demonstrates superior generalization, validating its robustness for IoV security applications.