Causal Inference for Heterogeneous Data and Information Theory

• Outros Autores: Hlaváčková-Schindler, Kateřina (Editor)
• Formato: Recurso Electrónico Capítulo de Livro
• Idioma: inglês
• Publicado em: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Assuntos: Information technology industries; Computer science; common hidden cause; graphical models; probabilistic models; Chain Event Graphs; interventions; causal calculus; causal fairness; responsible data science; causal discovery; Hawkes process; high-dimensional statistics; hidden confounder; causality; Bitcoin; inflation; yield spreads; approximation theory; Hellinger distance; Kullback-Leibler divergence; correct specification; misspecified models; causal inference; instrumental variables; neural networks; doubly robust estimation; semi-parametric theory; instrumental variable; causal graph; non-Gaussianity; causal graphs; dynamic systems; causal learning; time; continuous; event cognition; econometrics software; causal machine learning; statistical learning; conditional average treatment effects; individualized treatment effects; multiple treatments; selection-on-observables; piecewise linear; thresholds model; causal Inference; regularization; BART; Stan; machine learning; heterogeneous treatment effects; multilevel data; grouped data; artificial intelligence; higher-order category theory; statistics; n/a
• Acesso em linha: DOAB: download the publication; DOAB: description of the publication

Resumo

The present reprint, "Causal Inference for Heterogeneous Data and Information Theory", is a special issue of Journal Entropy. This Special Issue belongs to the section "Information Theory, Probability, and Statistics". The reprint gathers thirteen original contributions of leading experts in the theory of causal inference, focusing namely on the utilization of instrumental variables in a causal model, estimation of average treatment effect, the role of interventions in causal models, graphical causal modeling, causal algebras, causal modeling using the theory of categories, temporal causal model, heterogeneous data, and information-theoretic approaches.

Descrição Física

1 electronic resource (282 p.)

ISBN

books978-3-0365-8051-7
9783036580500
9783036580517

Acesso

Open Access