PC algorithm for nonparanormal graphical models

By Naftali Harris, Mathias Drton. 2013

176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marloes H. Maathuis
2. Mathias Drton
3. Christina Heinze-Deml
4. Nicolai Meinshausen
5. Preetam Nandy
6. Peter Buhlmann
7. Alain Hauser
8. Diego Colombo

In Control and Systems Engineering

1. Marloes H. Maathuis
2. Diego Colombo
3. Nicolai Meinshausen
4. Preetam Nandy
5. Alain Hauser
6. Christina Heinze-Deml
7. Mathias Drton
8. Robin J. Evans

In Statistics and Probability

1. Marloes H. Maathuis
2. Mathias Drton
3. Christina Heinze-Deml
4. Nicolai Meinshausen
5. Peter Buhlmann
6. Preetam Nandy
7. Diego Colombo
8. Alain Hauser

Citations in the corpus

• Causal Inference through a Witness Protection Program (2014, background): “Alternatively, one could test for the corresponding vanishing partial correlations in the empirical Spearman rank correlation matrix, as suggested by Harris and Drton (2013), at a particular significance level α.”
• Order-independent constraint-based causal structure learning (2012, methodology): “Constraint-based methods for causal structure learning use conditional independence tests to obtain information about the underlying causal structure.”
• Causal Structure Learning (2017, methodology): “The idea of Harris and Drton (2013) can also be applied to FCI, leading to rankFCI.”
• High-dimensional consistency in score-based and hybrid structure learning (2015, methodology): “A variant of the PC algorithm, called Rank PC, was shown to be consistent in high-dimensional settings under the nonparanormal distribution assumption [Harris and Drton (2013)].”
• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, unknown): “PC [223] 1993 constraint yes yes yes no CPDAG CCD [196] 1996 constraint yes yes both no PAG FCI [223] 2000 constraint no yes yes no PAG TPDA [31] 2002 constraint yes yes yes no CPDAG CPC [190] 2006 constraint yes relaxed yes no CPDAG KCL [227] 2007 constraint yes yes yes no CPDAG ION [234] 2008 cons”
• A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data (2014, unknown): “  For   accuracy   on   difficult   nonlinear,   non-‐ Gaussian  data  sets,  we  also  compare  a  recent  test  due  to  Harris  &  Drton  (2013),  applicable  to   nonlinear,   non-‐Gaussian   distributions   in   the   Gaussian   copula,   as   well   as   to   partial   correlation,  a  linear…”
• Structure Learning in Graphical Modeling (2016, background): “Harris & Drton (2013) generalize this result to Gaussian copulas.”