CausalShift: A Modular, Plugin-Based Framework for Dataset Shift Handling in Machine Learning
CausalShift is proposed, a modular, plugin-based framework for end-to-end dataset shift handling that reduces the in-distribution to out-of-distribution accuracy gap, while remaining competitive on real-world image shift and achieving performance parity with ERM on mild-shift tasks.