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.
arXiv:2606. 08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM).
arXiv:2411.05625v2 Announce Type: replace Abstract: We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variable pai...
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.
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.
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.
arXiv:2601.01368v2 Announce Type: replace Abstract: Score-based causal discovery in the presence of unobserved confounders requires both a consistent scoring criterion and an efficient search over gr...
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.
arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pai...
arXiv:2606. 05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.
arXiv:2609.16931v1 Announce Type: cross Abstract: We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the c...
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.
arXiv:2606. 19361v1 Announce Type: cross Abstract: Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available.