ADIGen generates counterfactuals under general interventions via Riesz regression, causal invariance, and orthogonal learning, with excess-risk bounds featuring product-bias remainder and invariant risk across environments.
arXiv preprint arXiv:2509.16842 , year =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
GANICE uses an extended Wasserstein distance and cellwise critic in a GAN to estimate conditional interventional distributions with minimax optimality guarantees.
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.
citing papers explorer
No citing papers match the current filters.