Root Cause Analysis of Outliers with Missing Structural Knowledge
Recent work conceptualized root cause analysis (RCA) of anomalies via quantitative
contribution analysis using causal counterfactuals in structural causal models (SCMs). The …
contribution analysis using causal counterfactuals in structural causal models (SCMs). The …
Root Cause Explanation of Outliers under Noisy Mechanisms
Identifying root causes of anomalies in causal processes is vital across disciplines. Once
identified, one can isolate the root causes and implement necessary measures to restore the …
identified, one can isolate the root causes and implement necessary measures to restore the …
Causal structure based root cause analysis of outliers
We describe a formal approach to identify'root causes' of outliers observed in $ n $ variables
$ X_1,\dots, X_n $ in a scenario where the causal relation between the variables is a known …
$ X_1,\dots, X_n $ in a scenario where the causal relation between the variables is a known …
TSLiNGAM: DirectLiNGAM under heavy tails
S Leyder, J Raymaekers, T Verdonck - arXiv preprint arXiv:2308.05422, 2023 - arxiv.org
One of the established approaches to causal discovery consists of combining directed
acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional …
acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional …
Causal structure-based root cause analysis of outliers
Current techniques for explaining outliers cannot tell what caused the outliers. We present a
formal method to identify" root causes" of outliers, amongst variables. The method requires a …
formal method to identify" root causes" of outliers, amongst variables. The method requires a …
Root cause identification for collective anomalies in time series given an acyclic summary causal graph with loops
This paper presents an approach for identifying the root causes of collective anomalies
given observational time series and an acyclic summary causal graph which depicts an …
given observational time series and an acyclic summary causal graph which depicts an …
Deconfounded score method: Scoring DAGs with dense unobserved confounding
A Bellot, M van der Schaar - arXiv preprint arXiv:2103.15106, 2021 - arxiv.org
Unobserved confounding is one of the greatest challenges for causal discovery. The case in
which unobserved variables have a widespread effect on many of the observed ones is …
which unobserved variables have a widespread effect on many of the observed ones is …
Ranking causal anomalies via temporal and dynamical analysis on vanishing correlations
Modern world has witnessed a dramatic increase in our ability to collect, transmit and
distribute real-time monitoring and surveillance data from large-scale information systems …
distribute real-time monitoring and surveillance data from large-scale information systems …
A Diagnostic Tool for Functional Causal Discovery
S Prakash, F Xia, E Erosheva - arXiv preprint arXiv:2406.07787, 2024 - arxiv.org
Causal discovery methods aim to determine the causal direction between variables using
observational data. Functional causal discovery methods, such as those based on the …
observational data. Functional causal discovery methods, such as those based on the …
Unsuitability of NOTEARS for causal graph discovery
M Kaiser, M Sipos - arXiv preprint arXiv:2104.05441, 2021 - arxiv.org
Causal Discovery methods aim to identify a DAG structure that represents causal
relationships from observational data. In this article, we stress that it is important to test such …
relationships from observational data. In this article, we stress that it is important to test such …