Diego Colombo

Artificial Intelligence · 2010–2012

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #2,891 of 35,345 by trust here, #5,682 by citations
• Statistics and Probability — 16 citations into it; #2,670 of 12,932 by trust here, #1,989 by citations
• Causal Inference & Discovery — 14 citations into it; #75 of 364 by trust here, #164 by citations
• Probabilistic & Bayesian Methods — 13 citations into it
• Information Systems — 5 citations into it
• Molecular Biology — 5 citations into it; #801 of 17,773 by trust here, #7,205 by citations

Whom Diego Colombo trusts overall

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

1. Jiji Zhang
2. Marloes H. Maathuis
3. Thomas S. Richardson
4. Peter Spirtes
5. Markus Kalisch
6. Christopher Meek
7. Tom L. Burr
8. Peter Buhlmann
9. Nicolai Meinshausen
10. Judea Pearl

Who trusts Diego Colombo overall

1. Marloes H. Maathuis
2. Diviyan Kalainathan
3. Preetam Nandy
4. Christina Heinze-Deml
5. Olivier Goudet
6. Jakob Runge
7. Jack Kuipers
8. Lu Zhang
9. Peter Buhlmann
10. Jan Peters

Papers from Diego Colombo's perspective

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

1. Equivalence and Synthesis of Causal Models
2. Learning Bayesian networks: The combination of knowledge and statistical data
3. Ancestral graph Markov models
4. Strong completeness and faithfulness in Bayesian networks
5. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
6. Causal Inference in the Presence of Latent Variables and Selection Bias
7. Causality : Models , Reasoning , and Inference
8. Causal inference and causal explanation with background knowledge
9. Learning Equivalence Classes of Bayesian Network Structures
10. High-dimensional graphs and variable selection with the Lasso

Their papers

• 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
• Order-independent constraint-based causal structure learning (2012) — 735 citations
• Predicting causal effects in large-scale systems from observational data (2010) — 297 citations