On Empirical Generalisations

  • Federica Russo
Part of the The Philosophy of Science in a European Perspective book series (PSEP, volume 3)


Manipulationism holds that information about the results of interventions is of utmost importance for scientific practices such as causal assessment or explanation. Specifically, manipulation provides information about the stability, or invariance, of the (causal) relationship between (variables) X and Y: were we to wiggle the cause X, the effect Y would accordingly wiggle and, additionally, the relation between the two will not be disrupted. This sort of relationship between variables are called ‘invariant empirical generalisations’. The paper focuses on questions about causal assessment and analyses the status of manipulation. It is argued that manipulationism is trapped in a dilemma. If manipulationism is read as providing a conceptual analysis of causation, then it fails to provide a story about the methods for causal assessment. If, instead, manipulationism is read as providing a method for causal assessment, then it is at an impasse concerning causal assessment in areas where manipulations are not performed. Empirical generalisations are then reassessed, in such a way that manipulation is not taken as methodologically fundamental. The paper concludes that manipulation is the appropriate tool for some scientific (experimental) contexts, but not for all.


Conceptual Analysis Causal Modelling Causal Theory Causal Assessment Causal Claim 
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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of KentCanterburyUK

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