Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints

@article{Jabbari2017DiscoveryOC,
  title={Discovery of Causal Models that Contain Latent Variables Through Bayesian Scoring of Independence Constraints},
  author={Fattaneh Jabbari and Joseph Ramsey and Peter Spirtes and Gregory F. Cooper},
  journal={Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD},
  year={2017},
  volume={2017},
  pages={
          142-157
        }
}
Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 35 references

Causation, Prediction, and Search

  • P. Spirtes, C. N. Glymour, R. Scheines
  • MIT Press, Cambridge
  • 2000
Highly Influential
4 Excerpts

Hailfinder: a Bayesian system for forecasting severe weather

  • B. Abramson, J. Brown, W. Edwards, A. Murphy, R. L. Winkler
  • Int. J. Forecast. 12(1), 57–71
  • 1996
Highly Influential
3 Excerpts

Structure learning in graphical modeling

  • M. Drton, M. H. Maathuis
  • Annu. Rev. Stat. Appl. 4, 365–393
  • 2016
1 Excerpt

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