Special Issue "Causality Theory: Computational Complexity, Algorithms and Applications"

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: 30 June 2021.

Special Issue Editors

Prof. Dr. Maciej Liśkiewicz
Website
Guest Editor
Institut für Theoretische Informatik, Universität zu Lübeck, 23538 Lübeck, Germany
Interests: algorithms; causality; cryptography; learning; computational complexity
Prof. Dr. Ralf Möller
Website
Guest Editor
Institut für Informationssysteme, Universität zu Lübeck, 23538 Lübeck, Germany
Interests: artificial intelligence; relational probabilistic graphical models; machine learning; intelligent agents; web and data science

Special Issue Information

Dear Colleagues,

Causal inference has recently become a rapidly growing field of interdisciplinary research that involves mathematical statistics, machine learning, and some sub-fields of artificial intelligence and computer science. The goal is to explore, from observed data and phenomena, the causal dependencies between different objects and actions, e.g., between medical treatment and recovery. The theory of causality being developed over the past decades provides an intuitive and sound model of causal relationships as partially directed graphs and allows for a mathematical description of real experiments with observational data. This approach is gaining increasing attention in epidemiology, sociology, and other empirical disciplines. The focus of this Special Issue is on the algorithmic and complexity aspects of causality: Despite the significant achievements made in causal theory, several key challenges of an algorithmic nature remain open. 

We invite you to submit original papers to this Special Issue on "Causality Theory: Computational Complexity, Algorithms, and Applications". The following is a (non-exhaustive) list of topics of interests: 

  • Algorithmic aspects of graphical causal models
  • Causal effects estimation
  • Efficient identification in linear structural causal models
  • Formal derivations of causal effects
  • Counting graphical models
  • Sampling graphical models
  • Algorithmic and complexity aspects of learning of causal models
  • Active learning of causal models
  • Relational causal dependence
  • Robust relational causal discovery

Prof. Dr. Maciej Liśkiewicz
Prof. Dr. Ralf Möller
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 papers will be 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 1400 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

  • Graphical causal model
  • Directed acyclic graph
  • Causal inference
  • do-calculus
  • Intervention
  • Markov equivalence
  • Instrumental variable
  • Covariate adjustment set
  • Linear structural causal model
  • Lifted relational causal reasoning

Published Papers

This special issue is now open for submission.
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