Machine Learning and Causal Inference
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 January 2024) | Viewed by 428
Special Issue Editor
Special Issue Information
Dear Colleagues,
The understanding of causality is a fundamental aspect of human intelligence. Causal inference is a broad field that seeks to understand cause-and-effect relationships between variables based on observational data or data obtained from experiments. The past decades have seen an increasing interaction between causal inference and machine learning methodologies, mainly in two folds: (1) applying machine learning to causal inference, i.e., utilizing graph learning, representation learning, reinforcement learning, etc., to help discover causal directions, estimate treatment effect, etc., and (2) applying causal inference to machine learning, i.e., leveraging structural causal models, potential outcome framework, counterfactual inference, etc., to address challenging problems in machine learning such as fairness, interpretability, robustness, trustworthiness, etc., in various domains including algorithmic decision-making, recommender system, computer vision, and natural language processing.
This Special Issue focuses on theoretical and methodological research that is relevant to any intersection of causality and machine learning. The Special Issue encourages submissions of original papers on topics including but not limited to: causal discovery, causal inference, counterfactual inference, graphical models, fair AI, explainable AI (XAI), transfer learning, causal representation learning, machine learning for decision-making, recommender system, computer vision, and natural language processing.
Dr. Lu Zhang
Guest Editor
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. Entropy 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 2600 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 structure discovery
- causal effect estimation
- causal representation learning
- causal generative models
- implications of the principle of independent causal mechanisms (ICM) for machine learning
- fairness, accountability, transparency, explainability and trustworthiness in artificial intelligence
- algorithmic recourse
- transfer learning and domain adaptation
- foundational theories of causal inference
- applications of causal inference to real-world problems
