Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies

TL;DR

This paper develops a new method for identifying robust predictors under latent distribution shifts using imperfect proxies, leveraging domain diversity and active learning to overcome limitations of existing proxy-based approaches.

Contribution

It introduces latent equivalent classes (LECs) and a domain diversity condition, enabling point-identification without the completeness assumption, and proposes the PQAL framework for efficient active learning.

Findings

Successfully recovers robust predictors on synthetic data.

Demonstrates robustness to varying degrees of distribution shift.

Outperforms previous methods on semi-synthetic datasets.

Abstract

Addressing the domain adaptation problem becomes more challenging when distribution shifts across domains stem from latent confounders that affect both covariates and outcomes. Existing proxy-based approaches that address latent shift rely on a strong completeness assumption to uniquely determine (point-identify) a robust predictor. Completeness requires that proxies have sufficient information about variations in latent confounders. For imperfect proxies the mapping from confounders to the space of proxy distributions is non-injective, and multiple latent confounder values can generate the same proxy distribution. This breaks the completeness assumption and observed data are consistent with multiple potential predictors (set-identified). To address this, we introduce latent equivalent classes (LECs). LECs are defined as groups of latent confounders that induce the same conditional proxy distribution. We show that point-identification for the robust predictor remains achievable as long as multiple domains differ sufficiently in how they mix proxy-induced LECs to form the robust predictor. This domain diversity condition is formalized as a cross-domain rank condition on the mixture weights, which is substantially weaker assumption than completeness. We introduce the Proximal Quasi-Bayesian Active learning (PQAL) framework, which actively queries a minimal set of diverse domains that satisfy this rank condition. PQAL can efficiently recover the point-identified predictor, demonstrates robustness to varying degrees of shift and outperforms previous methods on synthetic data and semi-synthetic dSprites dataset.