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Algorithms
Special Issues
Bayesian Networks and Causal Reasoning
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Bayesian Networks and Causal Reasoning

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 9046

Special Issue Editors

Dr. Kevin B Korb

E-Mail Website
Guest Editor
School of Historical and Philosophical Studies, Faculty of Arts, University of Melbourne, Parkville, VC 3010, Australia
Interests: philosophy of science; computer simulation; Bayesian reasoning; evaluation theory; argument analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Steven Mascaro

E-Mail Website
Co-Guest Editor
Bayesian Intelligence Pty Ltd, 21 Torry Hill Road, Upwey, VC 3158, Australia
Interests: causal discovery; Bayesian modelling; elicitation
Erik P. Nyberg

E-Mail Website
Co-Guest Editor
Department of Data Science & AI, Monash University, Clayton, VC 3800, Australia
Interests: philosophy of science; Bayesian networks; reasoning; causation; information; social epistemology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your research on using Bayesian Networks to understand and explain causal systems, and to inform decision making under conditions of uncertainty. The algorithms of interest include those embedded within systems using Bayesian Networks, those generating Bayesian Networks (e.g., causal discovery) and those applying Bayesian Networks in complex decision making and analysis, and include informal algorithms describing how humans may use Bayesian Networks for these purposes. New methods, analyses, and experimental results across this spectrum are most welcome. Problems addressed may range from fundamental or theoretical (e.g., What are causes and how should they be represented in a causal model?), to psychological (e.g., How can Bayesian Networks reduce susceptibility to causal fallacies?), to applied (e.g., How can we use causal Bayesian Networks to improve disease surveillance?).

Dr. Kevin B Korb
Dr. Steven Mascaro
Erik P. Nyberg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • causal bayesian networks
  • causal discovery
  • machine learning of bayesian networks
  • causal reasoning
  • causal inference
  • evidential reasoning
  • causal modelling
  • actual causation
  • causal criteria
  • type and token causation
  • statistical and causal fallacies
  • reasoning under uncertainty
  • bayesian decision making
  • prediction and explanation with bayesian networks
  • causal explanation
  • causal attribution
  • bayesian network applications

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Published Papers (4 papers)

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Research

24 pages, 2592 KiB  
Article
Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
by Maria Ganopoulou, Efstratios Kontopoulos, Konstantinos Fokianos, Dimitris Koparanis, Lefteris Angelis, Ioannis Kotsianidis and Theodoros Moysiadis
Algorithms 2024, 17(4), 138; https://doi.org/10.3390/a17040138 - 27 Mar 2024
Viewed by 1501
Abstract
Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal [...] Read more.
Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal discovery. This study explored causal discovery techniques within HRQoL, assessed various considerations for reliable estimation, and proposed means for interpreting outcomes. Five causal structure learning algorithms were employed to examine different aspects in structure estimation based on simulated data derived from HRQoL-related directed acyclic graphs. The performance of the algorithms was assessed based on various measures related to the differences between the true and estimated structures. Moreover, the Resource Description Framework was adopted to represent the responses to the HRQoL questionnaires and the detected cause–effect relationships among the questions, resulting in semantic knowledge graphs which are structured representations of interconnected information. It was found that the structure estimation was impacted negatively by the structure’s complexity and favorably by increasing the sample size. The performance of the algorithms over increasing sample size exhibited a similar pattern, with distinct differences being observed for small samples. This study illustrates the dynamics of causal discovery in HRQoL-related research, highlights aspects that should be addressed in estimation, and fosters the shareability and interoperability of the output based on globally established standards. Thus, it provides critical insights in this context, further promoting the critical role of HRQoL questionnaires in advancing patient-centered care and management. Full article
(This article belongs to the Special Issue Bayesian Networks and Causal Reasoning)
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23 pages, 5003 KiB  
Article
Active Data Selection and Information Seeking
by Thomas Parr, Karl Friston and Peter Zeidman
Algorithms 2024, 17(3), 118; https://doi.org/10.3390/a17030118 - 12 Mar 2024
Viewed by 2896
Abstract
Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the [...] Read more.
Bayesian inference typically focuses upon two issues. The first is estimating the parameters of some model from data, and the second is quantifying the evidence for alternative hypotheses—formulated as alternative models. This paper focuses upon a third issue. Our interest is in the selection of data—either through sampling subsets of data from a large dataset or through optimising experimental design—based upon the models we have of how those data are generated. Optimising data-selection ensures we can achieve good inference with fewer data, saving on computational and experimental costs. This paper aims to unpack the principles of active sampling of data by drawing from neurobiological research on animal exploration and from the theory of optimal experimental design. We offer an overview of the salient points from these fields and illustrate their application in simple toy examples, ranging from function approximation with basis sets to inference about processes that evolve over time. Finally, we consider how this approach to data selection could be applied to the design of (Bayes-adaptive) clinical trials. Full article
(This article belongs to the Special Issue Bayesian Networks and Causal Reasoning)
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14 pages, 975 KiB  
Article
What Is a Causal Graph?
by Philip Dawid
Algorithms 2024, 17(3), 93; https://doi.org/10.3390/a17030093 - 21 Feb 2024
Cited by 1 | Viewed by 1591
Abstract
This article surveys the variety of ways in which a directed acyclic graph (DAG) can be used to represent a problem of probabilistic causality. For each of these ways, we describe the relevant formal or informal semantics governing that representation. It is suggested [...] Read more.
This article surveys the variety of ways in which a directed acyclic graph (DAG) can be used to represent a problem of probabilistic causality. For each of these ways, we describe the relevant formal or informal semantics governing that representation. It is suggested that the cleanest such representation is that embodied in an augmented DAG, which contains nodes for non-stochastic intervention indicators in addition to the usual nodes for domain variables. Full article
(This article belongs to the Special Issue Bayesian Networks and Causal Reasoning)
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32 pages, 703 KiB  
Article
Entropy and the Kullback–Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation
by Marco Scutari
Algorithms 2024, 17(1), 24; https://doi.org/10.3390/a17010024 - 6 Jan 2024
Viewed by 1714
Abstract
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl’s causality, and determines their explainability and interpretability. Despite their popularity, there [...] Read more.
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl’s causality, and determines their explainability and interpretability. Despite their popularity, there are almost no resources in the literature on how to compute Shannon’s entropy and the Kullback–Leibler (KL) divergence for BNs under their most common distributional assumptions. In this paper, we provide computationally efficient algorithms for both by leveraging BNs’ graphical structure, and we illustrate them with a complete set of numerical examples. In the process, we show it is possible to reduce the computational complexity of KL from cubic to quadratic for Gaussian BNs. Full article
(This article belongs to the Special Issue Bayesian Networks and Causal Reasoning)
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