CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.
Causal discovery with continuous additive noise models.Journal of Machine Learning Research
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Interventional Time Series Priors for Causal Foundation Models
CausalTimePrior generates synthetic temporal structural causal models with paired observational and interventional time series to train prior-data fitted networks for in-context causal effect estimation on held-out data.