Learning high-dimensional directed acyclic graphs with latent and selection variables

@inproceedings{Colombo2012LearningHD,
  title={Learning high-dimensional directed acyclic graphs with latent and selection variables},
  author={Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
  year={2012}
}
  • Diego Colombo, Marloes H. Maathuis, +1 author Thomas S. Richardson
  • Published 2012
  • Mathematics, Computer Science
  • We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore propose the new RFCI algorithm, which is much faster than FCI. In some situations the output of RFCI… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 140 CITATIONS

    Order-independent constraint-based causal structure learning

    VIEW 7 EXCERPTS
    CITES BACKGROUND & METHODS

    Causal query in observational data with hidden variables

    VIEW 3 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs

    VIEW 5 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Outage Prediction and Diagnosis for Cloud Service Systems

    VIEW 4 EXCERPTS
    CITES METHODS
    HIGHLY INFLUENCED

    The Global Markov Property for a Mixture of DAGs

    VIEW 3 EXCERPTS
    CITES BACKGROUND
    HIGHLY INFLUENCED

    Comparative benchmarking of causal discovery algorithms

    VIEW 6 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    Causal Discovery Under Non-Stationary Feedback

    VIEW 10 EXCERPTS
    CITES BACKGROUND & METHODS
    HIGHLY INFLUENCED

    FILTER CITATIONS BY YEAR

    2012
    2020

    CITATION STATISTICS

    • 33 Highly Influenced Citations

    • Averaged 25 Citations per year from 2017 through 2019

    • 47% Increase in citations per year in 2019 over 2018

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 26 REFERENCES

    Ancestral graph Markov models

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    An Anytime Algorithm for Causal Inference

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    An algorithm for causal inference in the presence of latent variables and selection bias. In Computation, Causation, and Discovery 211–252

    • P. Spirtes, C. Meek, T. Richardson
    • 1999
    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Learning Equivalence Classes of Bayesian-Network Structures

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL