Causal Inference for Heterogeneous Data and Information Theory

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 the...

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Opis bibliograficzny
Format:	Online
Język:	angielski
Wydane:	MDPI - Multidisciplinary Digital Publishing Institute 2023
Hasła przedmiotowe:	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 thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries thema EDItEUR::U Computing and Information Technology::UY Computer science
Dostęp online:	ONIX_20230808_9783036580500_27
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!

Opis
Streszczenie:	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.

Causal Inference for Heterogeneous Data and Information Theory

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