CausalShift: A Modular, Plugin-Based Framework for Dataset Shift Handling in Machine Learning

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Abstract
Machine learning models deployed under distribution shift suffer performance degradation whose root cause is rarely diagnosed before adaptation is attempted. Existing frameworks either lack a closed loop from shift diagnosis to method selection, require a fully specified causal graph, or depend on multiple heterogeneous training environments—conditions rarely met in practice. We propose CausalShift, a modular, plugin-based framework for end-to-end dataset shift handling. A universal statistical core remains fully functional without any causal assumption; four optional causal plugins are activated selectively by a four-level knowledge grading system that scales from a fully specified structural causal model (Level A) down to a single observational dataset (Level D). A three-path causal attribution module estimates the relative contributions of covariate and concept shift to the performance gap, and attribution-driven routing maps the diagnosis to an adaptation strategy grounded in minimax optimality. Proof-of-concept experiments on four benchmarks—a synthetic structural causal model (SCM), Colored Modified National Institute of Standards and Technology (MNIST), Benchmarking In-the-Wild Distribution Shifts (WILDS) Camelyon17, and five American Community Survey (ACS) geographic shift tasks—demonstrate that CausalShift reduces the in-distribution to out-of-distribution (ID–OOD) accuracy gap by 19.9 percentage points on the synthetic benchmark and 72.5 percentage points on the spurious correlation benchmark, while remaining competitive on real-world image shift and achieving performance parity with ERM on mild-shift tasks. A tiered causal evaluation suite, including Λ*-robustness bounds and distribution-induced shift decomposition of error (DISDE), reveals robustness differences that scalar accuracy metrics cannot detect.

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