M
Markus Kalisch
Researcher at ETH Zurich
Publications - 40
Citations - 3821
Markus Kalisch is an academic researcher from ETH Zurich. The author has contributed to research in topics: Directed acyclic graph & Causal inference. The author has an hindex of 19, co-authored 40 publications receiving 3216 citations.
Papers
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Journal Article
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
Markus Kalisch,Peter Bühlmann +1 more
TL;DR: This work proves uniform consistency of the PC-algorithm for very high-dimensional, sparse DAGs where the number of nodes is allowed to quickly grow with sample size n, as fast as O(na) for any 0 < a < ∞.
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Causal Inference using Graphical Models with the R Package pcalg
TL;DR: The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data.
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Estimating high-dimensional intervention effects from observational data
TL;DR: This paper proposes to use summary measures of the set of possible causal effects to determine variable importance and uses the minimum absolute value of this set, since that is a lower bound on the size of the causal effect.
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Learning high-dimensional directed acyclic graphs with latent and selection variables
TL;DR: This work proposes the new RFCI algorithm, which is much faster than FCI, and proves consistency of FCI and RFCI in sparse high-dimensional settings, and demonstrates in simulations that the estimation performances of the algorithms are very similar.
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High-Dimensional Statistics with a View Toward Applications in Biology
TL;DR: This work reviews statistical methods for high-dimensional data analysis and pays particular attention to recent developments for assessing uncertainties in terms of controlling false positive statements (type I error) and p-values.