Markus Kalisch

Statistics and Probability · 2005–2015

A researcher in the Science corpus: 8 papers; trusts 62 researchers and is trusted by 74 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 78 citations into it; #1,355 of 12,932 by trust here, #1,123 by citations
• Artificial Intelligence — 55 citations into it; #3,437 of 35,345 by trust here, #5,281 by citations
• Causal Inference & Discovery — 26 citations into it; #53 of 364 by trust here, #82 by citations
• Probabilistic & Bayesian Methods — 21 citations into it; #315 of 1,525 by trust here, #477 by citations
• Statistical Learning Theory — 20 citations into it
• Molecular Biology — 11 citations into it; #2,385 of 17,773 by trust here, #8,905 by citations
• Optimization & Training — 11 citations into it
• Mathematical Optimization — 9 citations into it

Whom Markus Kalisch trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Peter Buhlmann
2. Nicolai Meinshausen
3. Tom L. Burr
4. David Maxwell Chickering
5. Marloes H. Maathuis
6. Judea Pearl
7. Peter Spirtes
8. Christopher Meek
9. Peng Zhao
10. Jiji Zhang

Who trusts Markus Kalisch overall

1. Peter Buhlmann
2. Marloes H. Maathuis
3. Alain Hauser
4. Diego Colombo
5. Preetam Nandy
6. Jack Kuipers
7. Christina Heinze-Deml
8. Jan Peters
9. Naftali Harris
10. Sara A. van de Geer

Papers from Markus Kalisch's perspective

Ordered by how strongly the researchers Markus Kalisch trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. High-dimensional graphs and variable selection with the Lasso
3. Equivalence and Synthesis of Causal Models
4. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
5. Estimating the Dimension of a Model
6. Causality : Models , Reasoning , and Inference
7. Optimal Structure Identification With Greedy Search
8. Sure independence screening for ultrahigh dimensional feature space
9. On Model Selection Consistency of Lasso
10. Causation

Their papers

• Estimating high-dimensional intervention effects from observational data (2008) — 415 citations
• Learning high-dimensional directed acyclic graphs with latent and selection variables (2011) — 534 citations
• Causal Inference Using Graphical Models with the R Package pcalg (2012) — 673 citations
• Predicting causal effects in large-scale systems from observational data (2010) — 297 citations
• Controlling false positive selections in high-dimensional regression and causal inference (2013) — 18 citations
• A Complete Generalized Adjustment Criterion (2015) — 82 citations
• High-Dimensional Statistics with a View Toward Applications in Biology (2014) — 323 citations
• Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (2005) — 1,070 citations