SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
Assaad, Emilie Devijver, and Eric Gaussier
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ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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Causal Discovery for Irregularly Time Series with Consistency Guarantees
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.