Causal Inference for Heterogeneous Data and Information Theory

• Weitere Verfasser: Hlaváčková-Schindler, Kateřina (Herausgegeben von)
• Format: Elektronisch Buchkapitel
• Sprache: Englisch
• Veröffentlicht: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Schlagworte: 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
• Online-Zugang: DOAB: download the publication; DOAB: description of the publication

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 leadin...