3 papers
math.ST2025
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…
stat.ME2025
Model Selection over Partially Ordered Sets
Armeen Taeb, Peter Bühlmann, Venkat Chandrasekaran
In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as presence or absence of a variable or an edge. Consequentl…
stat.ME2025
Causality-oriented robustness: exploiting general noise interventions
Xinwei Shen, Peter Bühlmann, Armeen Taeb
Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, suc…