The rationale of variation in methodological and evidential pluralism

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Russo, Federica
conference paper description
Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. The first developments of quantitative causal analysis in the social sciences are due to Quetelet (1869) and Durkheim (1895 and 1897) in demography and sociology respectively. Significant improvements are due to Blalock (1964) and Duncan (1975). Since then causal analysis has shown noteworthy progress in the formal methods of analysis, e.g., structural equation models, covariance structure models, multilevel models, and contingency tables. By means of these different methodologies, social scientists try to infer causal relations between variables of interest with reasonable confidence. Data comes from a variety of different sources: surveys, census, experiments, interviews, etc. Analogously, evidence of causal relations can come from different sources: previous studies, background knowledge, knowledge of mechanisms or of probabilistic relations, etc. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence do not entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. This view of causality profoundly breaks down with the received view, an heritage of Hume, that sees in regularity and/or invariance the key notions for causality. For instance, the rationale of variation clearly emerges in the interpretation of structural equation models: given a system of equations, parameters quantify the variation of the dependent variable due to the variation in the independent variable(s). Regularity and invariance thus become constraints to impose on the variation in order ensure that the model correctly specify the data generating process and that it does not confuse accidental and spurious relations with causal ones. Further, I argue that this monistic epistemology is also involved in alternative philosophical theories of causation, for instance, in probabilistic theories of causality, mechanist and counterfactual accounts, agency-manipulability theories and in the epistemic theory. The philosophical gain in adopting the rationale of variation is at least threefold. First, causality is not merely lodged in a psychological habit of observing regular successions of events. Agreed, we do experience such regular sequences but it is not because of regularity that we interpret them causally. Instead, this is because certain variational relations hold. Second, causality is not reduced to statistics either. Further, to claim that variation is a precondition for regularity and invariance has the advantage of not confusing the rationale of causality with the conditions that allow to interpret variations causally. Third, the adoption of the rationale of variation avoids confusing (i) what causality is (metaphysics) with the notion employed in testing (epistemology) and (ii) with the conditions – e.g. invariance – to impose on the variation to interpret it causally (methodology).