Post-graduate theses
Current Record: 10 of 833
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Identifier |
000463775 |
Title |
Automated causal discovery library |
Alternative Title |
Βιβλιοθήκη για την αυτόματη εύρεση αιτιακών σχέσεων |
Author
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Ντρουμπογιάννης, Αντώνιος Χ.
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Thesis advisor
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Τσαμαρδινός, Ιωάννης
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Reviewer
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Τριανταφύλλου, Σοφία
Τόλλης, Ιωάννης
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Abstract |
In the realm of data science and statistics, understanding the causal relation-
ships between variables is paramount for making informed decisions and predictions. Traditional methods of causal discovery, relying heavily on human expertise
and manual analysis, pose significant challenges in terms of scalability, objectivity,
and efficiency. To address these challenges, we introduce the concept of Automated
Causal Discovery (AutoCD), a revolutionary approach that seeks to fully automate
the application of causal discovery and causal reasoning methods. AutoCD is a
python library, designed not only to replicate the analytical capabilities of an ex-
pert human analyst but also to surpass them by leveraging advanced algorithms
and computational power. The goal of AutoCD is to comprehensively deliver all
causal information, including complex causal relationships and interactions, and
to efficiently answer a user’s causal queries with precision.
AutoCD represents a significant leap forward in the field of causal analysis by
automating the intricate process of identifying causal relationships from vast and
complex datasets. We describe the architecture of such a platform, which is built
upon a robust framework that integrates state-of-the-art algorithms for causal discovery and reasoning. This architecture is designed to be flexible, allowing for
customization and scalability according to the specific needs of various applications. Furthermore, we illustrate the performance of AutoCD on synthetic data
sets, showcasing its ability to accurately and efficiently uncover causal relationships.
The system’s general applicability is one of its core strengths, enabling it to
be deployed across a wide array of causal discovery problems. From healthcare
and epidemiology to economics and social sciences, AutoCD has the potential to
transform how causal analysis is conducted, making sophisticated causal discovery
accessible to a broader audience without the prerequisite of deep statistical knowledge. By automating the process of causal discovery, AutoCD not only enhances the efficiency and accuracy of causal analysis but also democratizes access to advanced causal reasoning capabilities, opening up new possibilities for research and decision-making across multiple domains.
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Language |
English |
Subject |
Artificial intelligence |
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Causality |
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Feature selection |
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Graphs |
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Machine learning |
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Python |
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Αιτιότητα |
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Γράφοι |
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Μηχανική μάθηση |
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Τεχνητή νοημοσύνη |
Issue date |
2024-03-22 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/2/6/1/metadata-dlib-1712242114-331307-21722.tkl
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Views |
256 |
Digital Documents
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It won't be available until: 2026-03-22
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