611 citations · 1.4k across the 9 of their papers we have counts for
4 papers · 1 filter
Learning high-dimensional directed acyclic graphs with latent and selection variables
Diego Colombo, Marloes H. Maathuis, Markus Kalisch +1
We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The…
Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
Peter Bühlmann, Markus Kalisch, Marloes H. Maathuis
We consider variable selection in high-dimensional linear models where the number of covariates greatly exceeds the sample size. We introduce the new concept of partial faithfulnes…
Decomposition and Model Selection for Large Contingency Tables
Corinne Dahinden, Markus Kalisch, Peter Bühlmann
Large contingency tables summarizing categorical variables arise in many areas. For example in biology when a large number of biomarkers are cross-tabulated according to their disc…
Estimating high-dimensional intervention effects from observational data
Marloes H. Maathuis, Markus Kalisch, Peter Bühlmann
We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but…