Hugging Face Trending Papers

Federated Causal Discovery via Regression-Directed Cumulants

The paper investigates federated learning for linear non‑Gaussian acyclic models (LiNGAM), proposing the FedRCD family of algorithms that use higher‑order cumulants to enable privacy‑preserving causal discovery across distributed clients. It addresses limitations of existing federated methods, such as FedISHC’s failure under near‑symmetric noise, and introduces variants that balance communication rounds with algebraic noise handling. Experiments reveal that cumulant‑based federated approaches rank variables by a variance ladder induced by the DAG rather than by population asymmetry, and that marginal standardisation degrades performance while scale‑invariant DirectLiNGAM remains robust.

arXiv Machine Learning

Sep 4

Federated Causal Discovery via Regression-Directed Cumulants

The paper introduces FedRCD, a family of federated causal discovery algorithms for linear non‑Gaussian acyclic models (LiNGAM). FedRCD overcomes limitations of the existing FedISHC method, which fails under near‑symmetric noise, by leveraging higher‑order cumulant tensors that aggregate across independent client data in a single communication round. Three variants of FedRCD trade off communication rounds against algebraic noise, with two exact federated counterparts of centralised HC and HC‑LiNGAM, and a single‑round variant that supports exact unlearning at any granularity.

By Pablo Torrijos, Fabio Stella, Jos\'e A. G\'amez, Jos\'e M. Puerta

safety

arXiv AI

Jun 24

A Survey on Federated Causal Discovery and Inference

arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.

By Xianjie Guo, Yuwei Wang, Guodu Xiang, Xiaoli Tang, Kui Yu, Han Yu, Qiang Yang

safety

arXiv Machine Learning

Jul 17

Skewness-Robust Causal Discovery in Location-Scale Noise Models

arXiv:2511. 14441v2 Announce Type: replace-cross Abstract: To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model.

By Daniel Klippert, Alexander Marx

benchmarks

arXiv AI

Jun 17

FoundCause: Causal Discovery with Latent Confounders from Observational Data

arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.

By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan

llms

arXiv AI

Jun 9

CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning

arXiv:2606. 08027v1 Announce Type: cross Abstract: Vertical federated learning (VFL) is a distributed learning paradigm that leverages vertically partitioned features across isolated parties without sharing raw samples; however, it remains vulnerable to active sample reconstruction attacks.

By Yongqi Jiang, Yansong Gao, Siguang Chen, Anmin Fu

safety

Hugging Face Trending Papers

Jul 7

Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants

Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG.

arXiv Machine Learning

Sep 22

A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression

arXiv:2609.22654v1 Announce Type: cross Abstract: Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. W...

By Brigham Halverson, Sharmistha Guha, Jessica Bernard, Rajarshi Guhaniyogi

safety

arXiv Machine Learning

Sep 2

Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms

arXiv:2502.20115v4 Announce Type: replace Abstract: Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In pract...

By Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyv\"arinen

arXiv Machine Learning

Jul 8

Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants

arXiv:2607. 05984v1 Announce Type: new Abstract: Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem.

By Ming Cai, Hisayuki Hara

arXiv Statistics ML

Aug 25

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects

The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.

By Wenlong Ji, Lihua Lei, Asher Spector

arXiv Machine Learning

Sep 1

Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...

By Haijie Xu, Chen Zhang

arXiv AI

Jun 3

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.

By Bo Peng, Kaiwen Wu, Sirui Chen, Zhiheng Wang, Yu Qiao, Chaochao Lu

llms

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