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Titlebook: Causality in Policy Studies; a Pluralist Toolbox Alessia Damonte,Fedra Negri Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author

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發(fā)表于 2025-3-21 18:18:22 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Causality in Policy Studies
副標(biāo)題a Pluralist Toolbox
編輯Alessia Damonte,Fedra Negri
視頻videohttp://file.papertrans.cn/223/222631/222631.mp4
概述This book is open access, which means that you have free and unlimited access.Provides a survey of methodological techniques for policy studies.Takes a realistic theoretical approach to causality.Incl
叢書名稱Texts in Quantitative Political Analysis
圖書封面Titlebook: Causality in Policy Studies; a Pluralist Toolbox Alessia Damonte,Fedra Negri Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author
描述.This volume provides a methodological toolbox for conducting policy research. Recognizing that policy research spans various academic disciplines, each of which takes a different view on causality, the volume introduces a methodologically pluralistic approach to policy studies. Each chapter clarifies the research question that each technique can answer, the research design and data treatment that each technique requires for its results to be sound, the validity domain of its results, and the actual deployment of the technique through a replicable example. Techniques covered include quasi-experimental designs, approaches to account for selection bias and observed imbalances, directed acyclic graphs and structural equation models, Qualitative Comparative Analysis, Bayesian case study and process tracing, and Agent-Based Modelling. By working through the volume, readers will understand how to learn from different techniques, apply them consciously, and triangulate them to make better sense of findings. This volume is intended for advanced academic courses, as well as scholars and practitioners in policy-related fields, such as political science, economics, sociology, and public admin
出版日期Book‘‘‘‘‘‘‘‘ 2023
關(guān)鍵詞causal inference; counterfactual impact evaluation; Qualitative Comparative Analysis; structural equati
版次1
doihttps://doi.org/10.1007/978-3-031-12982-7
isbn_softcover978-3-031-12984-1
isbn_ebook978-3-031-12982-7Series ISSN 2730-9614 Series E-ISSN 2730-9622
issn_series 2730-9614
copyrightThe Editor(s) (if applicable) and The Author(s) 2023
The information of publication is updating

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Introduction: The Elephant of Causation and the Blind Sages,d almost every discipline has its own. Researchers are like the blind sages who had never come across the elephant of causation before and who develop their idea of the elephant by “touching” a different part of it. Which part of the elephant will you happen to touch? Will you be able to listen to and understand what the other sages will tell you?
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發(fā)表于 2025-3-22 10:05:43 | 只看該作者
Conclusions. Causality Between Plurality and Unity,a plurality of objects and “incommensurable” interpretations. It also argues that the same evidence pinpoints how this plurality is complementary at every level, and causal structures raise as the elements that link ontology and methodology and can organize heterogeneous findings to improve learning across accounts.
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發(fā)表于 2025-3-22 16:49:01 | 只看該作者
Causation in the Social Realm,tecedents to consequents are less exact than those linking gas leaks to explosions. It is, therefore, a crucial challenge for the philosophy of social science to give a compelling account of causal reasoning about social phenomena that does justice to the research problems faced by social scientists.
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發(fā)表于 2025-3-22 20:14:16 | 只看該作者
Getting the Most Out of Surveys: Multilevel Regression and Poststratification,ing public opinion estimates to account for observed imbalances between the survey sample and population of interest. It covers the history of MRP, recent advances, an example analysis with code, and concludes with a discussion of best practices and limitations of the approach.
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https://doi.org/10.1007/978-3-663-02811-6chniques, smart statistical tools for reducing imbalance in the empirical distribution of pretreatment covariates between the treatment and control groups. Lastly, it provides an empirical illustration by focusing on two powerful algorithms: the entropy balancing (EB) and the coarsened exact matching (CEM). The chapter ends with caveats.
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