739 citations · 1.8k across the 34 of their papers we have counts for
4 papers · 2 filters
Invariance, Causality and Robustness
Peter Bühlmann
We discuss recent work for causal inference and predictive robustness in a unifying way. The key idea relies on a notion of probabilistic invariance or stability: it opens up new i…
Estimating heterogeneous treatment effects in nonstationary time series with state-space models
Shu Li, Peter Bühlmann
Randomized trials and observational studies, more often than not, run over a certain period of time. The treatment effect evolves during this period which provides crucial insights…
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…
Anchor regression: heterogeneous data meets causality
Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann +1
We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal…