Aiming at the feature ambiguity and misdetection problems in bird′s eye view lane line detection caused by environmental disturbances such as sudden changes in illumination and extreme weather, this paper proposes a causal interventionbased BEV lane detection framework. First, to enhance the representation of features during BEV spatial transformation, composite positional encoding is designed and fused to front view features to maintain spatial continuity and consistency. Second, the causal intervention module is constructed after acquiring the BEV features. The causal intervention module aims to explicitly decouple the lane line features from the environmental disturbances by generating counterfactual features to improve the stability of the model in extreme environments. Finally, the dynamic calibration of multi-scale features and interference suppression is accomplished by introducing the feature fusion module, and the global attention mechanism is utilized to achieve the enhancement of BEV features. The experimental results show that in the three subsets of the Apollo dataset, the F1 values are improved by 0.8%, 1%, and 3% compared to the model with the 2nd performance, and the F1 values are also optimal in the challenging scenarios within the OpenLane dataset that contain extreme weather, night, and intersections. The explicit decoupling of lane line features and environmental disturbances is successfully realized, providing a highly robust solution for autonomous driving perception in complex environments.