Deconfounding and Causal Regularization for Stability and External Validity

no code implementations • 14 Aug 2020 • Peter Bühlmann, Domagoj Ćevid

We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint.

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression

no code implementations • 29 May 2020 • Domagoj Ćevid, Loris Michel, Jeffrey Näf, Nicolai Meinshausen, Peter Bühlmann

Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm.

regression

Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding

1 code implementation • 8 Apr 2020 • Zijian Guo, Domagoj Ćevid, Peter Bühlmann

Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding.

Methodology Statistics Theory Statistics Theory