Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions.

Team semantics is a highly general framework for logics which describe dependencies and independencies among variables. Typically, the (in)dependencies considered in this context are properties of sets of configurations or data records. We show how team semantics can be further generalized to suppor...

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Publicado en:Journal of Philosophical Logic Vol. 50; no. 3; pp. 471 - 522
Autores principales: Barbero, Fausto, Sandu, Gabriel
Formato: Artículo
Publicado: Springer Nature Jun2021
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Jun2021
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      pub: Springer Nature
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        10.1007/s10992-020-09573-6
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        atl: Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions.
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          Barbero, Fausto
          Sandu, Gabriel
        affil: Department of Philosophy, History and Art studies, University of Helsinki, Unioninkatu 40, 00014, Helsinki, Finland
      su:
        Semantics
        Causal models
        Teams
        Structural equation modeling
        Teams in the workplace
      sug:
        subj:
          Semantics
          Causal models
          Teams
          Structural equation modeling
          Teams in the workplace
      keyword:
        Causation
        Dependence logic
        Interventionist counterfactual
        Manipulationist theories
        Structural equation models
        Team semantics
      ab: Team semantics is a highly general framework for logics which describe dependencies and independencies among variables. Typically, the (in)dependencies considered in this context are properties of sets of configurations or data records. We show how team semantics can be further generalized to support languages for the discussion of interventionist counterfactuals and causal dependencies, such as those that arise in manipulationist theories of causation (Pearl, Hitchcock, Woodward, among others). We show that the "causal teams" we introduce in the present paper can be used for modelling some classical counterfactual scenarios which are not captured by the usual causal models. We then analyse the basic properties of our counterfactual languages and discuss extensively the differences with respect to the Lewisian tradition.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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          year: 2021
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