What question did this study set out to answer?

To evaluate the high-dimensional consistency and applicability of the PC-algorithm for estimating directed acyclic graphs and their equivalence classes in Gaussian settings.

May 1, 2007Journal of Machine Learning Research

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm

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To evaluate the high-dimensional consistency and applicability of the PC-algorithm for estimating directed acyclic graphs and their equivalence classes in Gaussian settings.
Analyzed the constraint-based PC-algorithm for Gaussian graphical models where the number of nodes exceeds sample size.
Evaluated the recovery of the graph skeleton and Markov equivalence class under sparsity assumptions.
Demonstrates consistent estimation of directed acyclic graph skeletons in high-dimensional Gaussian settings.
Achieves reliable identification of Markov equivalence classes under sparsity conditions.

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Implication

Methodological study demonstrates high-dimensional directed acyclic graph estimation with the PC-algorithm, indicating its viability for sparse causal learning.

Cite This Study

KalischMarkus et al. (2007) studied this question.

synapsesocial.com/papers/6a7081352163a0a01bc4b877 https://doi.org/10.5555/1248659.1248681

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Implication

Methodological study demonstrates high-dimensional directed acyclic graph estimation with the PC-algorithm, indicating its viability for sparse causal learning.

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm

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Discussion

Implication

Cite This Study

Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm

Key Points

Discussion

Implication

Cite This Study

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