Under equal noise variance in structural VAR models, observational equivalence classes are defined by orthogonal transformations and a global positive scale, enabling the ENVAR procedure to identify sparse causal structures from the observed process.
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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 in Structural VAR Models Under Equal Noise Variance
Under equal noise variance in structural VAR models, observational equivalence classes are defined by orthogonal transformations and a global positive scale, enabling the ENVAR procedure to identify sparse causal structures from the observed process.
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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.