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stat.ME2020
High-recall causal discovery for autocorrelated time series with latent confounders
Andreas Gerhardus, Jakob 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.…
stat.ME2020
Reconstructing regime-dependent causal relationships from observational time series
Elena Saggioro, Jana de Wiljes, Marlene Kretschmer +1
Inferring causal relations from observational time series data is a key problem across science and engineering whenever experimental interventions are infeasible or unethical. Incr…