Toward Variation-Independent Regression by Composition

2026-04-19

AI Summary

This study addresses model bias in regression arising from dependence between the response variable and covariate variability by proposing a regression framework invariant to covariate distribution shifts, thereby circumventing the conventional reliance on data distributional stability. Built upon the “Regression by Composition” paradigm, the approach integrates principles from causal inference and structural modeling to construct composite models robust to changes in covariate distributions. The resulting framework significantly enhances model robustness under distributional shift. Theoretical analysis establishes the consistency and validity of the proposed method across diverse data-generating mechanisms, offering a novel paradigm for high-robustness regression modeling.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Discussion on "Regression by Composition" by Farewell, Daniel, Stensrud, and Huitfeldt.

Problem

• variation-independence
• regression
• composition
• causal inference
• statistical modeling

Innovation

• variation-independent
• regression by composition
• parameterization
• statistical modeling
• causal inference