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    Ethnographic Causality

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    Author(s)
    Abell, Peter
    Engel, Ofer
    Contributor(s)
    Wynn, Henry (other)
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/62229
    Keywords
    Singular Causality; Ethnographic Causality; Bayesian Narratives; Counterfactuals; Counterpotentials
    DOI
    10.21827/61b219bdd80d8
    ISBN
    9789403429472, 9789403429465
    Publisher
    University of Groningen Press
    Publisher website
    https://books.ugp.rug.nl/index.php/ugp
    Publication date and place
    Groningen, 2023
    Grantor
    • Rijksuniversiteit Groningen
    Classification
    Bayesian inference
    Pages
    142
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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