Approximation and Dependence via Multiteam Semantics

Durand, Arnaud; Hannula, Miika; Kontinen, Juha; Meier, Arne; Virtema, Jonni

doi:10.1007/978-3-319-30024-5_15

Cited by 10 publications

(9 citation statements)

References 34 publications

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“…One way to remedy to this problem is to replace the teams with multisets of assignments, known in the literature as multiteams. There at least two different approaches to the formalization of multiteams in the literature ( [33], [11]). However, the complex notational apparatus used in these two papers is mainly needed in order to handle quantification.…”

Section: Teams and Multiteamsmentioning

confidence: 99%

Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions

Barbero

Sandu

2020

J Philos Logic

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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.

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Section: Teams and Multiteamsmentioning

confidence: 99%

Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions

Barbero

Sandu

2020

J Philos Logic

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“…[26]) and modal logics ( [23]). Appropriate generalizations have been used as descriptive languages for probabilistic dependencies ( [5]), quantum phenomena ( [14]), Bayes nets ( [4]).…”

Section: Causal Teamsmentioning

confidence: 99%

“…A multiteam differs from a team in that it may feature multiple copies of the same assignment; it is therefore closer to a collection of experimental data than teams are. There have been at least two different approaches to the formalization of multiteams in the literature ( [24], [5]). For simplicity, we simulate multiteams by means of teams.…”

Section: Introducing Probabilitiesmentioning

confidence: 99%

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Interventionist Counterfactuals on Causal Teams

Barbero

Sandu

2019

Electron. Proc. Theor. Comput. Sci.

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We introduce an extension of team semantics ([13], [22]) which provides a framework for the logic of manipulationist theories of causation based on structural equation models, such as Woodward's ([25]) and Pearl's ([18]); our causal teams incorporate (partial or total) information about functional dependencies that are invariant under interventions. We give a unified treatment of observational and causal aspects of causal models by isolating two operators on causal teams which correspond, respectively, to conditioning and to interventionist counterfactual implication. We then introduce formal languages for deterministic and probabilistic causal discourse, and show how various notions of cause (e.g. direct and total causes) may be defined in them.Through the tuning of various constraints on structural equations (recursivity, existence and uniqueness of solutions, full or partial definition of the functions), our framework can capture different causal models. We give an overview of the inferential aspects of the recursive, fully defined case; and we dedicate some attention to the recursive, partially defined case, which involves a shift of attention towards nonclassical truth values. Structural equation modelsThe most basic objects in the structural equation modeling approach are variables, which we will denote with capital letters X,Y.... Each variable V can assume values (tipically denoted as v, v , v ...) within a certain range of objects, Ran(V ). Variables are related to each other by structural equations, for example Y := f Y (X 1 , . . . , X n )

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“…Finally, quantitative variants of Team Semantics such as the ones investigated in [3,4,17,18] have been recently attracted much interest, both because of their potential applications and because of their connections to problems in metafinite model theory. The same question about the classification of the logics based on them that have been discussed in the case of "classical" Team Semantics could be asked for these semantics, and their answer could do much to illuminate the properties of these more semantics and clarify the advantages and drawbacks of logics built on them.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

On Strongly First-Order Dependencies

Galliani

2016

Dependence Logic

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We prove that the expressive power of first-order logic with team semantics plus contradictory negation does not rise beyond that of first-order logic (with respect to sentences), and that the totality atoms of arity k + 1 are not definable in terms of the totality atoms of arity k. We furthermore prove that all first-order nullary and unary dependencies are strongly first order, in the sense that they do not increase the expressive power of first order logic if added to it.

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Approximation and Dependence via Multiteam Semantics

Cited by 10 publications

References 34 publications

Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions

Team Semantics for Interventionist Counterfactuals: Observations vs. Interventions

Interventionist Counterfactuals on Causal Teams

On Strongly First-Order Dependencies

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