arXiv Machine Learning July 28, 2026 By Ali Haghpanah Jahromi, Mohammad Taheri, Zohreh Azimifar

TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure

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arXiv:2607. 22762v1 Announce Type: cross Abstract: Causal inference has become a central issue across various fields, including computer science, statistics, economics, education, healthcare, and medicine.

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benchmarks

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

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.

By Henri Arno, Thomas Demeester

multimodal

arXiv Statistics ML

Aug 24

Double Machine Learning of Continuous Treatment Effects with Additive Instrumental Variables

The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.

By Shuyuan Chen, Peng Zhang, Yifan Cui

safety

arXiv Machine Learning

Aug 5

Causal Inference with Unstructured Outcomes

arXiv:2608. 03085v1 Announce Type: cross Abstract: Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives.

By Kevin Christian Wibisono, Yixin Wang

arXiv Statistics ML

Sep 22

Identification and Estimation under Multiple Versions of Treatment: Mixture-of-Experts Approach

arXiv:2601.00287v2 Announce Type: replace-cross Abstract: The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inferenc...

By Kohei Yoshikawa, Shuichi Kawano

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

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