arXiv Machine Learning June 2, 2026 By Xiaohui Yin, Avijit Mitra, Ying Zhou, Kun Chen, Hong Yu

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data

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arXiv:2606. 01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly when outcomes are rare.

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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.

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arXiv Machine Learning

Jun 5

Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction

arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.

By Amirhossein Zare, Amirhessam Zare, Herlock Rahimi, Reza Salarikia, Mohammad Kashkooli

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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.

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arXiv Machine Learning

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A reproducible and extensible framework for benchmarking competing risks survival models

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arXiv Statistics ML

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Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach

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arXiv Machine Learning

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Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation

arXiv:2602. 12379v2 Announce Type: replace Abstract: Estimating longitudinal treatment effects is essential for sequential decision-making but is challenging due to treatment-confounder feedback.

By Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

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