Counterfactual Model Testing Under Distribution Shift

Joran Leest, Ilias Gerostathopoulos, Claudia Raibulet, Patricia Lago

Deployed machine learning (ML) models can degrade as the distribution of their input data shifts. Such shifts can result from changes in the population or from data integrity issues in the pipeline that materializes model features, two sources that operate through different mechanisms. Because labels are often absent or delayed, it is common practice to track shifts in the data instead, but a detected shift does not indicate how it affects the model. We formulate this problem as online model testing under distribution shift. Testing requires reproducing the shift on a controlled baseline with known labels. Since the same shift can come from either source, doing so requires knowing which mechanism changed. We propose a methodology that reproduces observed changes in both mechanisms to estimate current performance and the potential gain from repairing observed data integrity issues. Experiments on real-world datasets with simulated faults show that separating the two sources generally improves both estimates, supporting this formulation. A churn prediction case illustrates how these tests can support monitoring and repair prioritization