6.4k citations · 14.8k across the 73 of their papers we have counts for
4 papers · 2 filters
On the pitfalls of Gaussian likelihood scoring for causal discovery
Christoph Schultheiss, Peter Bühlmann
We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecific…
Distributionally robust and generalizable inference
Dominik Rothenhäusler, Peter Bühlmann
We discuss recently developed methods that quantify the stability and generalizability of statistical findings under distributional changes. In many practical problems, the data is…
Treatment Effect Estimation with Observational Network Data using Machine Learning
Corinne Emmenegger, Meta-Lina Spohn, Timon Elmer +1
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in s…
Random Forests for Change Point Detection
Malte Londschien, Peter Bühlmann, Solt Kovács
We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability p…