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- Markus Kalisch, Peter Bühlmann
- J. Mach. Learn. Res.
- 2007

We consider the PC-algorithm ([13]) for estimating the skeleton of a very high-dimensional acyclic directed graph (DAG) with corresponding Gaussian distribution. The PC-algorithm is computationally… (More)

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 FCI (Fast Causal… (More)

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 it is possible to… (More)

The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this document, we give a brief overview of… (More)

We review statistical methods for high-dimensional data analysis and pay particular attention to recent developments for assessing uncertainties in terms of controlling false positive statements… (More)

- Marloes H. Maathuis, Diego Colombo, Markus Kalisch, Peter Bühlmann
- Nature Methods
- 2010

Supplementary Figure 1 Comparing IDA, Lasso and Elastic-net on the five DREAM4 networks of size 10 with multifactorial data. Supplementary Table 1 Comparing IDA, Lasso and Elastic-net to random… (More)

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 faithfulness and use it to… (More)

- Corinne Dahinden, Markus Kalisch, Peter Bühlmann
- Biometrical journal. Biometrische Zeitschrift
- 2010

Large contingency tables summarizing categorical variables arise in many areas. One example is in biology, where large numbers of biomarkers are cross-tabulated according to their discrete expression… (More)

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 valid covariates… (More)

- Emilija Perkovic, Johannes C Textor, Markus Kalisch, Marloes H. Maathuis
- J. Mach. Learn. Res.
- 2017

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 ancestral graphs… (More)