arXiv Machine Learning August 4, 2026 By Daan Caljon, Jente Van Belle, Wouter Verbeke

Estimating Treatment Effects in Networks under Unknown Exposure Mappings

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arXiv:2510. 21457v2 Announce Type: replace Abstract: Estimating heterogeneous treatment effects in network settings is complicated by interference, meaning that the outcome of an instance can be influenced by the treatment status of others.

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arXiv Machine Learning

Sep 4

Causal Foundation Models

Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.

By Christopher Stith, Hossein Rahmani, Jesse C. Cresswell

llms fine-tuning

arXiv AI

Sep 15

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...

By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas

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arXiv AI

Jun 2

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data

arXiv:2605. 24358v3 Announce Type: replace-cross Abstract: Estimating individual treatment effect (ITE) from observational graph data is crucial for decision-making in the fields such as commerce and medicine.

By Xiaofeng Lin, Han Bao, Hisashi Kashima

arXiv Machine Learning

Aug 4

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.

By Omar Faruque, Pavan Raj Ravi, Jianwu Wang

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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 AI

Sep 1

Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly i...

By Shuomin Xue, Jingyuan Li, Ju Jia, Jingxuan Yu, Xiaojun Jia

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Causal Foundation Models

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

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Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL