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Zhilong Mao

2 accepted papers

2026

A Causal Target for Learning to Defer Under Hidden Confounding

AAAI 2026technical

Learning decision policies from confounded observational data is a challenging task in causal inference, as unobserved confounders can lead to biased or suboptimal actions when relying solely on machine learning models. A synergistic approach is learning to defer, which decides when to act itself an

Cited by 0SourcePDFScholar
2026

Prototype-based Causal Intervention for Multi-Label Image Classification

CVPR 2026

Modern multi-label image classification models suffer from a critical reliance on spurious correlations, failing to learn the underlying causal mechanisms. Many causality-inspired methods are impractical, demanding box-level supervision that is rarely available in real-world datasets. Others rely on

Cited by 0SourcecodeScholar