3 papers
stat.ME2026
Causal Invariance Learning via Efficient Nonconvex Optimization
Zhenyu Wang, Yifan Hu, Peter Bühlmann +1
Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome a…
cs.LG2025
Natural Language-Based Synthetic Data Generation for Cluster Analysis
Michael J. Zellinger, Peter Bühlmann
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of th…
stat.ME2024
Spectral Deconfounding for High-Dimensional Sparse Additive Models
Cyrill Scheidegger, Zijian Guo, Peter Bühlmann
Many high-dimensional data sets suffer from hidden confounding which affects both the predictors and the response of interest. In such situations, standard regression methods or al…