arXiv:2607. 12145v1 Announce Type: cross Abstract: True causal relationships are rarely known, and inferring causal graphs from data is hard.
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.
arXiv:2607. 10456v1 Announce Type: cross Abstract: Expert background knowledge is often available in practical applications of causal discovery.
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: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:2607. 27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail.
arXiv:2606. 07525v1 Announce Type: cross Abstract: Causal graphs in text are typically populated by observable, predefined events.
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:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
MuPlon is a new framework for claim verification that models claims and evidence as a fully connected Claim‑Evidence Graph. It tackles two confounding problems—data noise and data biases—by applying a dual causal intervention strategy: a back‑door path that adjusts node weights to reduce noise and strengthen relevant connections, and a front‑door path that extracts key subgraphs, builds reasoning paths, and uses counterfactual reasoning to remove biases. Experiments show that MuPlon surpasses existing methods and achieves state‑of‑the‑art performance.
arXiv:2609.39406v1 Announce Type: new Abstract: Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlati...
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:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.