Corpus ID: 220364527

High-recall causal discovery for autocorrelated time series with latent confounders

@article{Gerhardus2020HighrecallCD,
  title={High-recall causal discovery for autocorrelated time series with latent confounders},
  author={Andreas Gerhardus and J. Runge},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.01884}
}
  • Andreas Gerhardus, J. Runge
  • Published 2020
  • Computer Science, Mathematics, Psychology
  • ArXiv
  • We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason. Information-theoretical arguments show that effect size can often be increased if causal parents are… CONTINUE READING

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    References

    SHOWING 1-10 OF 31 REFERENCES
    Causal Inference on Time Series using Restricted Structural Equation Models
    • 59
    • Highly Influential
    • PDF
    Search for Additive Nonlinear Time Series Causal Models
    • 64
    • PDF
    A Linear Non-Gaussian Acyclic Model for Causal Discovery
    • 761
    • PDF
    Causal discovery and inference: concepts and recent methodological advances
    • 127
    Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference
    • 25
    • PDF
    Causation, prediction, and search
    • 3,331
    • PDF