GANICE uses an extended Wasserstein distance and cellwise critic in a GAN to estimate conditional interventional distributions with minimax optimality guarantees.
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Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.
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Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
GANICE uses an extended Wasserstein distance and cellwise critic in a GAN to estimate conditional interventional distributions with minimax optimality guarantees.
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Debiased Counterfactual Generation via Flow Matching from Observations
Observational and counterfactual distributions are linked by identical support and invariant features, enabling a flow-matching estimator with semiparametric efficiency correction to generate debiased counterfactuals from observations.