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Editorial Special Issue on Causality: Fundamental Limits and Applications

Year: 2023

Type: editorial

Abstract: Causal determinism, is deeply ingrained with our ability to understand the physical sciences and their explanatory ambitions. Besides understanding phenomena, identifying causal networks is important for effective policy design in nearly any avenue of interest, be it epidemiology, financial regulation, management of climate, etc. This special issue covers several areas where causal inference research intersects with information theory and machine learning.

Source: IEEE Journal on Selected Areas in Information Theory

DOI: 10.1109/jsait.2024.3350468

Authors: Negar Kiyavash, Elias Bareinboim, Todd Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak

Institutions: École Polytechnique Fédérale de Lausanne, Columbia University, Stanford University, The University of Texas at Austin, Max Planck Institute for Intelligent Systems, Carnegie Mellon University, University of Wisconsin–Madison

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4391497941

Cites: 4

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Related to: 10