Causality and Causal Modelling in the Social Sciences Measuring Variations /

• Κύριος συγγραφέας: Russo, Federica (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Dordrecht : Springer Netherlands, 2009.
• Σειρά: Methodos Series ; 5
• Θέματα: Social sciences.; Philosophy and social sciences.; Statistics.; Economics.; Management science.; Population.; Demography.; Social Sciences.; Methodology of the Social Sciences.; Economics, general.; Philosophy of the Social Sciences.; Population Economics.; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law.

The anti-causal prophecies of last century have been disproved. Causality is neither a ‘relic of a bygone’ nor ‘another fetish of modern science’; it still occupies a large part of the current debate in philosophy and the sciences. This investigation into causal modelling presents the rationale of c...

Πίνακας περιεχομένων:

• Preface
• Introduction
• 1: Scope of the book and methodology
• 2: Structure of the book
• 3: Philosophical issue in the back of the mind
• 4: Philosophy at the service of social research
• 5: Open problems: causal realism, objectivity, and social ontology
• 1: What do social scientists do?- Introduction
• 1.1: Different causal claims?- 1.2: Smoking and lung cancer
• 1.3: Mother’s education and child survival
• 1.4: Health and wealth
• 1.5: Farmer’s migration
• 1.6: Job satisfaction
• 1.7: Methodological and epistemological morals
• 2: Probabilistic approaches
• Introduction
• 2.1: Philosophical accounts: Good and Suppes
• 2.2: probabilistic theories: traditional criticisms
• 2.3: Brining causal theory to maturity
• 3: Methodology of causal modeling
• Introduction
• 3.1: Methods and assumptions of causal modeling
• 3.1.1: Path models and causal diagrams
• 3.1.2: Covariance structure models
• 3.1.3: Granger-causality
• 3.1.4: Rubin’s model
• 3.1.5: Multilevel analysis
• 3.1.6: Contingency tables
• 3.2: Hypothetico-deductive methodology
• 3.3: Difficulties and weaknesses of causal modeling
• 4: Epistemology of causal modeling
• Introduction
• 4.1: The rationale of causality: Measuring variations
• 4.2: Varieties of variations
• 4.3: Wha guarantees the causal interpretation?- 4.3.1: Associational models
• 4.3.2: Causal models
• 5: Methodological consequences: objective Bayesianism
• Introduction
• 5.1: Probabilistic causal inferences
• 5.2: Interpretations of probability
• 5.3: The case for frequency-driven epistemic probabilities
• 6: Methodological consequences: mechanisms and levels of causation
• Introduction
• 6.1: Mechanisms
• 6.1.1" Modelling mechanisms
• 6.1.2: Mixed mechanisms
• 6.1.3 Explaining through mechanisms
• 6.1.4: Modelling causal mechanisms vs. modeling decision-making processes
• 6.2: Levels of causation
• 6.2.1: Twofold causality
• 6.3: Levels of analysis
• 6.3.1: Types of variables and of fallacies
• 6.3.2: Levels of analysis vs. levels of causation
• 6.3.3: Levels of analysis
• 6.3.4: Levels of analysis and variation in multilevel models
• 7: Supporting the rationale of variations
• Introduction,- 7.1: Variation in mechanist approaches
• 7.2: Variation in counterfactuals
• 7.3: Variation in agency theories
• 7.4: Variation in manipulability theories
• 7.5: Variation in epistemic causality
• 7.6: Variation in single instances: concluding remarks
• 1: Objectives, methodology, and results
• 2: The methodological import of philosophical results
• References
• Index.