New Method Enables Robust Prediction Under Domain Shift with Imperfect Proxy Variables
Researchers developed a technique to identify robust predictors when data distributions shift across domains due to hidden confounding factors, even when available proxy variables are imperfect. The approach replaces the traditional strong "completeness" assumption with a weaker "cross-domain rank condition" that leverages differences in how domains mix latent equivalent classes. This advance matters because it makes domain adaptation more practical in real-world scenarios where perfect proxy information is unavailable.
The paper addresses a fundamental challenge in domain adaptation: when latent confounders cause distribution shifts between domains, existing methods require proxies (observable variables related to hidden confounders) to contain complete information about those confounders. This completeness assumption is often violated in practice. The authors introduce the concept of latent equivalent classes (LECs)—groups of latent confounders that produce identical proxy distributions—and show that point-identification of a robust predictor is still possible if domains differ sufficiently in how they combine these LECs. They formalize this requirement as a cross-domain rank condition, which is substantially weaker than completeness. The proposed Proximal Quasi-Bayesian Active Learning (PQAL) framework actively selects diverse domains satisfying this condition and can recover the point-identified predictor. Experiments on synthetic and semi-synthetic datasets (dSprites, IHDP, ACS Folktables) demonstrate robustness to varying degrees of shift and improvements over prior methods.
What's missing
The paper does not discuss computational complexity or scalability of the PQAL framework to high-dimensional settings. Additionally, the practical guidance on how to verify the cross-domain rank condition in real applications without ground truth is not detailed.
What different sources said
- arXiv cs.LGCenter
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
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