The paper introduces Causal Posterior Estimation (CPE), a new Bayesian inference technique for simulator models where the likelihood is intractable but simulation is easy. CPE uses flow matching to approximate the posterior while embedding the model’s conditional dependencies directly into the neural network architecture. Experiments show that this hard‑coded structure yields highly accurate inference, matching or surpassing current state‑of‑the‑art methods.
By Simon Dirmeier, Antonietta Mira
arXiv:2607. 19866v1 Announce Type: new Abstract: Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research.
By Zheng Li, Hao Zhang, Ruxin Wang, Ruichu Cai, Kun Zhang, Feng Xie
Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research. Existing causal discovery methods either learn a global causal structure, which incurs substantial computational cost, or assume the absence of latent variables and selection bias, assumptions that are often violated in real-world settings.
arXiv:2606. 26467v1 Announce Type: new Abstract: We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems.
By Max Zhu, Martino Mansoldo, Ching-Hao Wang, Stefan Groha
arXiv:2608.21079v1 Announce Type: new
Abstract: Adverse Pregnancy Outcomes (APOs) such as preterm birth and gestational diabetes can have long-term consequences for both the mother and child, yet an...
By Kavimayil P. Komarasamy, Saurabh Mathur, Ameet Soni, David M. Haas, Kristian Kersting, Sriraam Natarajan
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, supports queries on all three levels of Pearl's Causal Hierarchy and uses known graph structure when available to improve predictions.
arXiv:2602. 01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs).
By Edwin V. Bonilla, He Zhao, Daniel M. Steinberg
arXiv:2607. 22934v1 Announce Type: cross Abstract: Learning causal graphs from interventional data is a challenging problem with broad applications.
By Yichen Gu, Yuxuan Song, Weizhou Qian, Yixin Wang, Joshua Welch
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
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: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
The paper reviews modern causal discovery techniques that go beyond conditional independence, highlighting how distributional properties, such as asymmetries, conditional moments, and environmental variation, can identify causal structure. It distinguishes between identification sources, statistical estimators, and computational implementations, and discusses challenges in the presence of latent confounding, feedback, and cyclic models. The authors also outline consistency, uncertainty quantification, and algorithmic guarantees, and provide guidance for comparing methods and a research agenda for future work.
By Yang Ni