Reprint

Causal Inference for Heterogeneous Data and Information Theory

Edited by
July 2023
282 pages
  • ISBN978-3-0365-8050-0 (Hardback)
  • ISBN978-3-0365-8051-7 (PDF)

This book is a reprint of the Special Issue Causal Inference for Heterogeneous Data and Information Theory that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary

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.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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 discovery; causal inference; causal graphs; dynamic systems; causal learning; time; continuous; event cognition; interventions; econometrics software; causal machine learning; statistical learning; conditional average treatment effects; individualized treatment effects; multiple treatments; selection-on-observables; causal inference; instrumental variables; piecewise linear; thresholds model; causal Inference; instrumental variables; neural networks; doubly robust estimation; regularization; BART; Stan; causal inference; machine learning; heterogeneous treatment effects; multilevel data; grouped data; artificial intelligence; higher-order category theory; causality; machine learning; statistics; n/a