arXiv Machine Learning
Jul 15
Falsifying Causal Graphs With Outlier Events
arXiv:2607. 12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard.
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
arXiv:2510. 06995v2 Announce Type: replace-cross Abstract: We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes.
Related stories
arXiv AI
Aug 24
Root cause analysis via difference graph discovery from linear time-series data
The paper investigates root cause analysis for anomalies in linear time-series data by using difference graph discovery. It focuses on effect-defying root causes—variables whose causal coefficients differ between normal and anomalous regimes—within linear discrete-time dynamic structural causal models. The authors adapt existing difference graph methods to the time-series context, evaluate them on simulated data, and apply them to real-world IT and intensive care monitoring datasets to localize causal mechanisms behind anomalous behavior.
arXiv Machine Learning
Jun 5
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
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 Machine Learning
Jul 31
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
arXiv:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.
arXiv Machine Learning
Sep 18
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
The paper addresses causal discovery in Directed Acyclic Graphs where nodes are either ordinal (modeled with an ordered logit) or follow a one‑parameter exponential family distribution. It proves that the direction of edges between such nodes is identifiable for generic parameter values, extending prior Ordinal‑Poisson results. The authors also propose a score‑based exhaustive search and a masked continuous optimization method using DAGMA, and demonstrate through simulations that these approaches recover orientations that are otherwise unidentifiable under classical structural equation models.
arXiv Machine Learning
Jun 5
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv Machine Learning
Jul 14
Integrating Background Knowledge for Scalable Causal Discovery
arXiv:2607. 10456v1 Announce Type: cross Abstract: Expert background knowledge is often available in practical applications of causal discovery.
arXiv AI
Jul 3
Actual causality in fault trees
arXiv:2607. 01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question "what can go wrong?
arXiv Machine Learning
Aug 27
Cluster-Dags as Powerful Background Knowledge For Causal Discovery
The paper introduces Cluster-DAGs as a flexible prior knowledge framework to improve causal discovery. It presents two modified constraint‑based algorithms, Cluster‑PC and Cluster‑FCI, tailored for fully and partially observed data. Experiments on simulated data show that these methods outperform baseline algorithms that lack prior knowledge.
arXiv Machine Learning
Sep 17
Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.
arXiv AI
Jul 13
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
arXiv:2607. 09449v1 Announce Type: new Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference.
arXiv Machine Learning
Jun 5
Robust Causal Discovery in Real-World Time Series with Power-Laws
arXiv:2507. 12257v4 Announce Type: replace Abstract: Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climate science.