611 citations · 1.4k across the 9 of their papers we have counts for
9 papers
Hierarchical inference for genome-wide association studies: a view on methodology with software
Claude Renaux, Laura Buzdugan, Markus Kalisch +1
We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). It is in part a review but covers also new developments for meta analysis wi…
Interpreting and using CPDAGs with background knowledge
Emilija Perković, Markus Kalisch, Maloes H. Maathuis
We develop terminology and methods for working with maximally oriented partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a Markov…
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
Emilija Perković, Johannes Textor, Markus Kalisch +1
We present a graphical criterion for covariate adjustment that is sound and complete for four different classes of causal graphical models: directed acyclic graphs (DAGs), maximum…
A Complete Generalized Adjustment Criterion
Emilija Perković, Johannes Textor, Markus Kalisch +1
Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify…
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…