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.

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safety

arXiv Machine Learning

Jul 27

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

arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.

By Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst

arXiv AI

Aug 5

Robust Counterfactual Policy Optimisation via Nondeterministic Causal Models

arXiv:2608. 02893v1 Announce Type: cross Abstract: Counterfactual inference approaches for sequential decision-making typically assume deterministic causal models, where all randomness stems from latent variables.

By Jessica Lally, Milad Kazemi, Nicola Paoletti, David Watson, Sander Beckers

reinforcement-learning

arXiv Machine Learning

Jul 17

Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

arXiv Machine Learning

Jun 3

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.

By Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Ga\"el Varoquaux, Matthieu Labeau

benchmarks safety

arXiv AI

Jul 8

Safe Bayesian Optimization with Counterfactual Policies

arXiv:2607. 05620v1 Announce Type: cross Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold.

By Katherine Avery, Bruno Castro da Silva, David Jensen

safety

arXiv Machine Learning

Sep 4

Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift

The paper introduces a semiparametric framework for counterfactual regression along a specified incremental‑intervention path. It estimates a finite‑dimensional constrained projection of counterfactual risk using cross‑fitted influence‑function representations, and establishes consistency, local stability, and first‑order expansions for smooth and finite‑dimensional programs. The results provide asymptotically valid inference, including simultaneous confidence bands, and are demonstrated through simulations and an SMS reminder application.

By Kwangho Kim

One email a morning, machine-written

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