arXiv AI August 20, 2026 By Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Marc Schoenauer, \"Ozg\"ur \c{S}im\c{s}ek

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

Read the original on arXiv AI →

CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

3d ago

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

arXiv:2605. 31156v2 Announce Type: replace Abstract: Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.

By Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang, Han-Jia Ye

llms benchmarks

arXiv AI

Jun 2

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

arXiv:2605. 02640v2 Announce Type: replace Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge.

By Ruta Binkyte, Ivaxi Sheth, Zhijing Jin, Mohammad Havaei, Bernhard Sch\"olkopf, Mario Fritz

safety

arXiv Machine Learning

Jun 17

Instrumental and Proximal Causal Inference with Gaussian Processes

arXiv:2603. 02159v2 Announce Type: replace-cross Abstract: Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding.

By Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau

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.

By Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst

arXiv AI

Jul 13

Tuning Derivatives for Causal Fairness in Machine Learning

arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.

By Filip Edstr\"om, Guilherme W. F. Barros, Tetiana Gorbach, Xavier de Luna

safety

arXiv AI

Aug 6

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602. 06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts.

By Junqi Chen, Sirui Chen, Chaochao Lu

llms reinforcement-learning fine-tuning benchmarks

One email a morning, machine-written

Related stories

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

Instrumental and Proximal Causal Inference with Gaussian Processes

Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness

Tuning Derivatives for Causal Fairness in Machine Learning

Can Post-Training Transform LLMs into Causal Reasoners?