Multi-modality MRI lesion segmentation leverages complementary contrasts across modalities to better characterize lesion morphology and tissue alterations, thereby improving lesion delineation and reducing misdiagnosis. Existing correlation-driven methods estimate fusion weights by converting cross-modality correlation scores into adaptive fusion weights. However, in multi-center settings, domain shift changes cross-modal correlation patterns, which breaks the learned correlation-to-weight mapping and leads to modality-weight bias that amplifies center-specific shortcuts. To address this issue, we propose an Intervention-based Weight Calibration Network (IWCNet) that models domain shift as a controllable embedding and leverages explicit interventions to mitigate its impact on fusion weight estimation. IWCNet constructs original and intervened branches by performing interventions on the domain embedding, measuring inter-branch prediction effects to calibrate fusion weights. IWCNet further introduces Operator-level Domain Modulation (OLDM) to construct structure-consistent domain-variant feature pairs via operator-level domain intervention, and Effect-Driven Consistency Calibration (EDCC) to jointly leverage intervention effects and cross-branch prediction consistency to calibrate weights. Extensive experiments on multi-center multi-modality MRI datasets demonstrate that IWCNet substantially improves segmentation accuracy and robustness under domain shift.