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

3 Jul 2020Andreas GerhardusJakob Runge

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... (read more)

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