Russo F. Causality and Causal Modelling in the Social Sciences: Measuring Variations

Springer – 2008, 245 pages
ISBN: 1402088167The first book to focus on the notion of variation in causal reasoning
Includes an accessible overview of the methodology of causal modelling
Provides a thorough discussion of philosophical accounts of causality
Bridges the gap between philosophy and the social sciences
Presents a defence of objective Bayesianism in causal modelling
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 causality, i.e. the notion that guides causal reasoning in causal modelling. It is argued that causal models are regimented by a rationale of variation, not of regularity nor invariance, thus breaking down the dominant Humean paradigm. The notion of variation is shown to be embedded in the scheme of reasoning behind various causal models: e.g. Rubin’s model, contingency tables, and multilevel analysis. It is also shown to be latent—yet fundamental—in many philosophical accounts. Moreover, it has significant consequences for methodological issues: the warranty of the causal interpretation of causal models, the levels of causation, the characterisation of mechanisms, and the interpretation of probability.
This book offers a novel philosophical and methodological approach to causal reasoning in causal modelling and provides the reader with the tools to be up to date about various issues causality rises in social science.Scope of the book and methodology
Structure of the book
Philosophical issue in the back of the mind
Philosophy at the service of social research
Open problems: causal realism, objectivity, and social ontology
What do social scientists do?Different causal claims?
Smoking and lung cancer
Mother’s education and child survival
Health and wealth
Farmer’s migration
Job satisfaction
Methodological and epistemological morals
Probabilistic approachesPhilosophical accounts: Good and Suppes
probabilistic theories: traditional criticisms
Brining causal theory to maturity
Methodology of causal modelingMethods and assumptions of causal modeling
Path models and causal diagrams
Covariance structure models
Granger-causality
Rubin’s model
Multilevel analysis
Contingency tables
Hypothetico-deductive methodology
Difficulties and weaknesses of causal modeling
Epistemology of causal modelingThe rationale of causality: Measuring variations
Varieties of variations
Wha guarantees the causal interpretation?
Associational models
Causal models
Methodological consequences: objective BayesianismProbabilistic causal inferences
Interpretations of probability
The case for frequency-driven epistemic probabilities
Methodological consequences: mechanisms and levels of causationMechanisms
Modelling mechanisms
Mixed mechanisms
Explaining through mechanisms
Modelling causal mechanisms vs. modeling decision-making processes
Levels of causation
Twofold causality
Levels of analysis
Types of variables and of fallacies
Levels of analysis vs. levels of causation
Levels of analysis
Levels of analysis and variation in multilevel models
Supporting the rationale of variations
Introduction,-Variation in mechanist approaches
Variation in counterfactuals
Variation in agency theories
Variation in manipulability theories
Variation in epistemic causality
Variation in single instances: concluding remarks
Objectives, methodology, and results
The methodological import of philosophical results