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230515 ||| eng |
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|a 9789403429472
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|a 61b219bdd80d8
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| 100 |
1 |
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|a Abell, Peter
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| 245 |
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|a Ethnographic Causality
|h Elektronische Ressource
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| 260 |
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|a Groningen
|b University of Groningen Press
|c 2023
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| 300 |
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|a 1 electronic resource (142 p.)
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| 653 |
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|a thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference
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| 653 |
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|a Bayesian inference / bicssc
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| 653 |
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|a Singular Causality; Ethnographic Causality; Bayesian Narratives; Counterfactuals; Counterpotentials
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| 700 |
1 |
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|a Engel, Ofer
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
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| 028 |
5 |
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|a 10.21827/61b219bdd80d8
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| 856 |
4 |
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|u https://library.oapen.org/bitstream/20.500.12657/62229/1/Ethnographic%2bCausality.pdf
|7 0
|x Verlag
|3 Volltext
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/99092
|z DOAB: description of the publication
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| 082 |
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|a 500
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| 520 |
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|a This book explores the problem of causal inference when a sufficient number of comparative cases cannot be found, which would permit the application of frequency based models formulated in terms of explanatory causal generalizations. Recent developments in causal inference focus mainly on type-causal mechanisms, where one type of event (such as greenhouse gas emissions) causes another type of event (such as climate change). In contrast, this book focuses on singular causation. Since it is inferred from testimonial evidence, singular causation is of interest in ethnographic case studies, where comparisons between cases and generalizations are not always possible. The book develops the notion of Bayesian and Comparative Narratives, using them to marshal evidence for singular causal connections from ethnographically elicited, indicative, counterfactual, and counterpotential statements. While preserving the universal concept of causality, the book explores its specific rather than general dimension. By addressing their complementary roles in causal identification and estimation, ""quantitative"" and ""qualitative"" approaches find common ground.
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