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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| Format: | Online |
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| Langue: | anglais |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Accès en ligne: | ONIX_20230808_9783036580500_27 |
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| _version_ | 1869519933246799872 |
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| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-112459 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1124592024-03-30T12:51:24Z Causal Inference for Heterogeneous Data and Information Theory Hlaváčková-Schindler, Kateřina 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 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. 2023-08-08T15:12:51Z 2023-08-08T15:12:51Z 2023 book ONIX_20230808_9783036580500_27 9783036580500 9783036580517 https://directory.doabooks.org/handle/20.500.12854/112459 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/7572 https://mdpi.com/books/pdfview/book/7572 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-8051-7 10.3390/books978-3-0365-8051-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036580500 9783036580517 282 Basel open access |
| spellingShingle | 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 Causal Inference for Heterogeneous Data and Information Theory |
| title | Causal Inference for Heterogeneous Data and Information Theory |
| title_full | Causal Inference for Heterogeneous Data and Information Theory |
| title_fullStr | Causal Inference for Heterogeneous Data and Information Theory |
| title_full_unstemmed | Causal Inference for Heterogeneous Data and Information Theory |
| title_short | Causal Inference for Heterogeneous Data and Information Theory |
| title_sort | causal inference for heterogeneous data and information theory |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20230808_9783036580500_27 |