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math.ST2025
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Yihong Gu, Cong Fang, Yang Xu +2
Pursuing invariant prediction from heterogeneous environments opens the door to learning causality in a purely data-driven way and has several applications in causal discovery and…
math.ST2024
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
Yihong Gu, Cong Fang, Peter Bühlmann +1
Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for a…