Ethnographic Causality

Description

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 generalisations. _Hlk108542951 _Hlk114524897 _Hlk108605241 _Hlk30350418 _Hlk62034155 _Hlk62047888 _Hlk62049130 _Hlk108610684 _Hlk62035221 _Hlk62040402 _Hlk62025662 chapter-1-introduction _Hlk59707092 _Hlk30345709 _Hlk61797160 X86d09851ddd2cadeb2f950b3d6b3fe0df4bd790 what-has-the-large-n-approach-achieved X6f76d70853a3d1846e777070f2302991b39e27a _Hlk61815258 _Ref61815075 _Hlk30356589 regression-and-causality _Hlk30356704 _Hlk61816026 bayesian-narratives _Hlk61817398 _Ref61817359 _Ref61851555 mechanisms-as-confounders _Ref61817850 _Hlk114506120 X91f686c00ad151755c1a365d113ebc9c411b991 X9c59406fce9b78ecf33e9814d14c10309a35c26 X7f2018f8d3846059cdcb2b0e2d47ef642abd98f X9bb3e9febac578a84705a24929c50c16cd3cf10 _Ref61820217 bayesian-inference-to-credible-beliefs _Hlk62042330 _Hlk62042340 _Hlk62043062 X7bc965c83f315008c7fc0805052aae0660c92ab meta-ethnography X14049109a4600f067163085e00b38598fb747b2 _Ref61820427 _Ref62040824 _Hlk61848767 _Ref61820552 _Ref61820859 Xcf2b72336d2e0ec7debb7f9a943c6cd4e370fcf _Hlk30362312 _Hlk30362899 _Ref61853166 macro-causality X8d7142d0292721cdd6960c0abd42df8c2dca80d conclusions X0621b1c6f07e0ce747de3136c31b7a6e860b454 the-logic-of-norms role-expectations role-structures-and-role-theory Preface 1. Introduction 2. Large and Small ​N​ Causal Inference: The Role of Comparison and Generalisation 2.1 Small ​N​ causal Inference 2.2 Small and Large N 2.3 Large ​N​ Causal Analysis 2.4 What has the Large ​N​ Approach Achieved? 2.5 An Introduction to Bayesian Narratives 2.6 Potential Outcomes and Counterfactual Causal Analysis in large ​N​ Studies: The Role of Inter-Unit Comparison 2.7 Causal Analysis in Large ​N​ Observational Studies 2.8 The Role of Causal Mechanisms In Large Studies 3. Ethnographic Causality: and Bayesian Narratives 3.1 Elicited Subjective Causal and Counterfactual Statements 3.2 Subjective Counter-Potentials 3.3 Singular Causality 3.4 Generalising Singular Ethnographic Causal Explanations 3.5 An Introduction to an Illustrative Empirical Example 3.6 Constructing a Case in Accordance with Ethnographic Causality 3.7 Bayesian Inference to Credible Causal Beliefs 3.8 From Credible Causal Beliefs to Justified Belief in Causal Connections 3.9 Meta-Ethnography 3.10 An Illustrative Empirical Example 3.11 Constructing Bayesian Narratives 3.12 Comparative Bayesian Narratives 3.13 The Interplay of Large ​N​ Causal Networks and Narratives 4. Multiple Levels of Causality 4.1 The General Framework, for large ​N​ Multi Level Analysis 4.2 Large ​N​ Causality Between Micro and Macro Networks 4.2 Macro-Causality in The large ​N​ Framework? 4.3 Ethnographic (Small-​N​) Causality in Multilevel Networks 4.4 Conclusions 5. Role Theory, Social Norms and Ethnographic Causality The Logic of Norms Role Expectations Role Structures and Causal Analysis 6. Bibliography 7. Appendix: A non-Technical Introduction to Networks and Graph Theory Figure ‎2.1 Mean ​​R​​ 2​​ values published in the American Sociology Review over the years (based on Abell and Koumenta, 2019) Figure ‎2.2 A causal network with error terms Figure 2.3 The Role of Causal Mechanisms Figure 2.4 Mechanism Y as a measured confounder Figure 3.1 The basic causal/teleological model {C} and {X} → (“act” {Y}) →T {Z} Figure ‎3.2 A Bayesian narrative strings together multiple singular causal links Figure ‎3.3 3 A Markov model of causal links Figure 4.1 The Coleman diagram Figure ‎4.2 An elaboration of Coleman’s diagram Figure ‎4.3 The distribution of node properties across networks of relationships Figure ‎7.1 A simple network Lege pagina Lege pagina