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.
MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback
arXiv:2604. 23107v2 Announce Type: replace-cross Abstract: Causal effect estimation from observational data requires careful adjustment for confounding.
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arXiv Machine Learning
Aug 11
Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
arXiv:2608. 08288v1 Announce Type: new Abstract: Estimating counterfactual outcomes over time from longitudinal observational data is central to clinical decision support.
arXiv AI
Jun 17
FoundCause: Causal Discovery with Latent Confounders from Observational Data
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
arXiv AI
Aug 12
Expert-Guided g-computation with Large Language Models for Estimating Causal Effects on Timings: Applications to Hospital Quality Improvement
arXiv:2608. 10339v1 Announce Type: cross Abstract: Hospital quality improvement (QI) programs routinely face multiple candidate interventions to optimize hospital flow, but existing methods struggle to estimate and rank the causal effects of such interventions.
arXiv AI
Jul 7
Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion
arXiv:2607. 02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems.
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.
arXiv AI
Jul 29
Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records
arXiv:2507. 20993v4 Announce Type: replace-cross Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text.
arXiv Machine Learning
Aug 7
Surv-IPTB: An Attention-Based Model for Estimating Individual Probability of Treatment Benefit with Survival Data
arXiv:2608. 06288v1 Announce Type: new Abstract: This work presents a novel attention-based framework for estimating the Individual Probability of Treatment Benefit (IPTB) in survival analysis contexts.
arXiv AI
Jul 2
CausalMix: Data Mixture as Causal Inference for Language Model Training
arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.
arXiv Machine Learning
Jun 9
MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
arXiv:2606. 08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints.
arXiv AI
Jun 11
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
Hugging Face Trending Papers
Jun 2
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
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.