Hugging Face Trending Papers June 2, 2026

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

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Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance.

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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 Machine Learning

Jul 14

Causal Foundation Models with Continuous Treatments

arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.

By Christopher Stith, Medha Barath, Vahid Balazadeh, Jesse C. Cresswell, Rahul G. Krishnan

llms fine-tuning benchmarks

arXiv Machine Learning

Jun 26

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.

By Haoran Zhang, Chuanpu Li, Yuxin Fu, Bin Tong, Guan Wang, Bo Zheng, Feng Zhou

rag benchmarks safety

Hugging Face Trending Papers

Jun 25

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios. In this paper, we propose the Cross-Head Attention Uplift Network (CHAUN) and Robust Adversarial Inverse Propensity Score (RA-IPS) method to address these limitations.

rag benchmarks safety

arXiv Machine Learning

Jul 16

PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

arXiv:2607. 13428v1 Announce Type: new Abstract: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones.

By Xutao Wang, Hanting Chen, Tianyu Guo, Yunhe Wang

safety

arXiv Machine Learning

Jun 8

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

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.

By Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina

safety

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