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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…
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…