arXiv Machine Learning July 1, 2026 By Xiaxian Ou, Razieh Nabi

Coarsening Bias from Variable Discretization in Causal Functionals

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arXiv:2602. 22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with hidden variables.

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

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arXiv Machine Learning

Jun 30

Multiply Robust Causal Mediation Analysis with Continuous Treatments

arXiv:2105. 09254v4 Announce Type: replace-cross Abstract: In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest.

By Yizhen Xu, AmirEmad Ghassami, Numair Sani, Ilya Shpitser

arXiv AI

Jul 17

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.

By Yichen Xu

llms diffusion benchmarks safety

arXiv Machine Learning

Jul 7

Two Layers of Instability in Causal Estimation

arXiv:2606. 21185v2 Announce Type: replace-cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed.

By Alexis Bellot

multimodal

arXiv Machine Learning

Jul 10

Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration

arXiv:2607. 08122v1 Announce Type: new Abstract: Workload-based differentially private (DP) synthetic data methods privately measure aggregate queries and post-process the noisy answers into synthetic records.

By Amir Asiaee, Kaveh Aryan

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arXiv Machine Learning

Jul 17

Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

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

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