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, nor of regularity neither invariance, thus breaking down the dominant Human 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.
‘Dr. Federica Russo’s book is a very valuable addition to a small number of relevant publications on causality and causal modelling in the social sciences viewed from a philosophical approach’. (Prof. Guillaume Wunsch, Institute of Demography, University of Louvain, Belgium)
Table of Content
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. levelsof 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.
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