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.
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.
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: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: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:2505. 15215v3 Announce Type: replace-cross Abstract: Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable.
arXiv:2608. 03772v1 Announce Type: new Abstract: Explaining the predictions of neural networks is a central challenge in trustworthy AI.