6.4k citations · 14.8k across the 73 of their papers we have counts for
18 papers · 1 filter
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…
Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann +1
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure,…
Invariant Causal Prediction for Sequential Data
Niklas Pfister, Peter Bühlmann, Jonas Peters
We investigate the problem of inferring the causal predictors of a response from a set of explanatory variables . Classical ordinary least squares regressi…
Causal inference in partially linear structural equation models
Dominik Rothenhäusler, Jan Ernest, Peter Bühlmann
We consider identifiability of partially linear additive structural equation models with Gaussian noise (PLSEMs) and estimation of distributionally equivalent models to a given PLS…
A sequential rejection testing method for high-dimensional regression with correlated variables
Jacopo Mandozzi, Peter Bühlmann
We propose a general, modular method for significance testing of groups (or clusters) of variables in a high-dimensional linear model. In presence of high correlations among the co…
Discussion: "A significance test for the lasso"
Peter Bühlmann, Lukas Meier, Sara van de Geer
Discussion of "A significance test for the lasso" by Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani [arXiv:1301.7161].