Using Unsupervised Learning to Help Discover the Causal Graph
- URL: http://arxiv.org/abs/2009.10790v1
- Date: Tue, 22 Sep 2020 20:07:19 GMT
- Title: Using Unsupervised Learning to Help Discover the Causal Graph
- Authors: Seamus Brady
- Abstract summary: AitiaExplorer is an exploratory causal analysis tool which uses unsupervised learning for feature selection.
A problem statement and requirements for the software are outlined.
It is found that AitiaExplorer meets these requirements and is a useful exploratory causal analysis tool.
- Score: 0.76146285961466
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The software outlined in this paper, AitiaExplorer, is an exploratory causal
analysis tool which uses unsupervised learning for feature selection in order
to expedite causal discovery. In this paper the problem space of causality is
briefly described and an overview of related research is provided. A problem
statement and requirements for the software are outlined. The key requirements
in the implementation, the key design decisions and the actual implementation
of AitiaExplorer are discussed. Finally, this implementation is evaluated in
terms of the problem statement and requirements outlined earlier. It is found
that AitiaExplorer meets these requirements and is a useful exploratory causal
analysis tool that automatically selects subsets of important features from a
dataset and creates causal graph candidates for review based on these features.
The software is available at https://github.com/corvideon/aitiaexplorer
Related papers
- Causal Reasoning in Software Quality Assurance: A Systematic Review [11.887059800587672]
This study provides a systematic review of the scientific literature on causal reasoning for SQA.
Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain.
tools to favour their application are appearing at a fast pace - most of them after 2021.
arXiv Detail & Related papers (2024-08-30T10:34:11Z) - Towards Extracting Ethical Concerns-related Software Requirements from App Reviews [0.0]
This study analyzes app reviews of the Uber mobile application (a popular taxi/ride app)
We propose a novel approach that leverages a knowledge graph (KG) model to extract software requirements from app reviews.
Our framework consists of three main components: developing an ontology with relevant entities and relations, extracting key entities from app reviews, and creating connections between them.
arXiv Detail & Related papers (2024-07-19T04:50:32Z) - IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements [50.57072342894621]
We discuss the benefits of exploiting known data dependencies and distribution constraints for Neurosymbolic use cases.
This opens a new research agenda with general questions about Neurosymbolic background knowledge and the expressivity required of its logic.
arXiv Detail & Related papers (2024-04-30T12:09:53Z) - Towards Self-Interpretable Graph-Level Anomaly Detection [73.1152604947837]
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection.
We propose a Self-Interpretable Graph aNomaly dETection model ( SIGNET) that detects anomalous graphs as well as generates informative explanations simultaneously.
arXiv Detail & Related papers (2023-10-25T10:10:07Z) - Causal Discovery and Counterfactual Explanations for Personalized
Student Learning [0.0]
The study's main contributions include using causal discovery to identify causal predictors of student performance.
The results reveal the identified causal relationships, such as the influence of earlier test grades and mathematical ability on final student performance.
A major challenge remains, which is the real-time implementation and validation of counterfactual recommendations.
arXiv Detail & Related papers (2023-09-18T10:32:47Z) - PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps)
It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
arXiv Detail & Related papers (2023-06-20T09:55:10Z) - AVIS: Autonomous Visual Information Seeking with Large Language Model
Agent [123.75169211547149]
We propose an autonomous information seeking visual question answering framework, AVIS.
Our method leverages a Large Language Model (LLM) to dynamically strategize the utilization of external tools.
AVIS achieves state-of-the-art results on knowledge-intensive visual question answering benchmarks such as Infoseek and OK-VQA.
arXiv Detail & Related papers (2023-06-13T20:50:22Z) - Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability.
We develop a new dataset containing ten new types of queries with features that have never been considered.
Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
arXiv Detail & Related papers (2023-04-14T11:35:35Z) - Analysing the Predictivity of Features to Characterise the Search Space [1.5484595752241122]
A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states.
In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches.
The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.
arXiv Detail & Related papers (2022-09-11T20:04:17Z) - Competency Problems: On Finding and Removing Artifacts in Language Data [50.09608320112584]
We argue that for complex language understanding tasks, all simple feature correlations are spurious.
We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account.
arXiv Detail & Related papers (2021-04-17T21:34:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.