arXiv Machine Learning June 8, 2026 By Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

Read the original on arXiv Machine Learning →

arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness

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