arXiv AI June 30, 2026 By Zineb Senane, Axel Karlsson, Lele Cao, Oleg Smirnov, Cheng Zhang, Sahar Asadi, Hedvig Kjellstr\"om, Gustav Eje Henter, Ruibo Tu

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework

Read the original on arXiv AI →

arXiv:2406. 08311v3 Announce Type: replace-cross Abstract: Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pairwise correlations largely unmeasured.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

22h 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 Machine Learning

Jul 8

Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure

arXiv:2603. 10254v2 Announce Type: replace Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains.

By Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin, Giovanni Cin\`a

benchmarks safety data-engineering

arXiv Machine Learning

Jun 9

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603. 10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced tabular settings to fully learn the complex data distribution.

By Xiaofeng Lin, Seungbae Kim, Zhuoya Li, Zachary DeSoto, Charles Fleming, Guang Cheng

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 14

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.

By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui

arXiv AI

Jul 10

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

arXiv:2607. 08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use.

By Andrej Leban, Yuekai Sun

llms agents benchmarks

arXiv Machine Learning

Jul 14

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).

By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang

llms benchmarks

Related stories

TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery

Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms