Causal Inference for Heterogeneous Data and Information Theory (Editorial)

0573813 - ÚI 2024 CH eng J - Journal Article
Hlaváčková-Schindler, Kateřina
Causal Inference for Heterogeneous Data and Information Theory (Editorial).
Entropy. Roč. 25, č. 6 (2023), č. článku 910. E-ISSN 1099-4300
R&D Projects: GA ČR(CZ) GA19-16066S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
Impact factor: 2.7, year: 2022
Method of publishing: Open access

The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference. The issue presents thirteen original contributions that span various topics, namely the role of instrumental variables in causal inference, the estimation of average treatment effects and the temporal causal models. Four papers are devoted to the design of novel causal models using interventions. The contributions use approaches of information theory, probability, algebraic structures, neural networks and with them related machine learning tools. The papers range from the theoretical ones, the paper applying the models, to the papers providing software tools for causal inference. All papers were peer-reviewed and accepted for publication due to their highest quality contribution. Here, we shortly preview the topics of the contributions.
Permanent Link: https://hdl.handle.net/11104/0344169