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- Markus Kalisch, Peter Bühlmann
- Journal of Machine Learning Research
- 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 feasible for sparse problems with many nodes, i.e. variables, and it has the attractive property to automatically achieve high computational efficiency as a… (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 consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper,… (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 Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally… (More)

The pcalg package for R (R Development Core Team (2010)) 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 the methodology, and demonstrate the package’s functionality in both toy examples and applications.

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

editorial office: 75 Varick Street, Fl 9, New York, NY 10013-1917. Tel (212) 726 9200, Fax: (212) 689 9702. annual subscription rates: USA/ Canada: US$150 (personal), US$2,513 (institution), Canada add 5% GST #104911595RT001; Euro-zone: €153 (personal), €1,997. (institution); UK and Europe £99 (personal), £1,288 (institution); Rest of world (excluding… (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 level. Interactions of the variables are of great interest and are generally studied with log-linear models. The structure of a log-linear model can be… (More)

- Markus Kalisch, Bernd Fellinghauer, +4 authors Gerold Stucki
- BMC medical research methodology
- 2010

BACKGROUND
Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined… (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 Fast Causal Inference algorithm (FCI) (Spirtes et al., 1999) has been explicitly designed to infer conditional independence and causal information in such settings. Despite its… (More)

March 19, 2014 Version 2.0-2 Date 2014-03-12 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch <kalisch@stat.math.ethz.ch> Title Methods for graphical models and causal inference Description This package contains several functions for causal structure learning and causal inference using graphical models. The main… (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 consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we… (More)