18 citations · 21 across the 3 of their papers we have counts for
4 papers
Deconfounding and Causal Regularization for Stability and External Validity
Peter Bühlmann, Domagoj Ćevid
We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability,…
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Domagoj Ćevid, Loris Michel, Jeffrey Näf +2
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construct…
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Zijian Guo, Domagoj Ćevid, Peter Bühlmann
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding. We focus on a high-dimensional linear regr…
Spectral Deconfounding via Perturbed Sparse Linear Models
Domagoj Ćevid, Peter Bühlmann, Nicolai Meinshausen
Standard high-dimensional regression methods assume that the underlying coefficient vector is sparse. This might not be true in some cases, in particular in presence of hidden, con…