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.
Author(s): Peter Abell, Ofer Engel
Publisher: University of Groningen Press
Year: 2023
Language: English
Pages: 139
City: Groningen
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chapter-1-introduction
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regression-and-causality
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bayesian-narratives
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mechanisms-as-confounders
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meta-ethnography
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macro-causality
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conclusions
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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
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