Revisiting causality using stochastics: 1. Theory

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dc.abstract.enCausality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Budownictwa i Geoinżynierii
dc.contributor.authorKoutsoyiannis, Demetris
dc.contributor.authorOnof, Christian
dc.contributor.authorChristofides, Antonis
dc.contributor.authorKundzewicz, Zbigniew W.
dc.date.accessioned2025-12-05T13:55:10Z
dc.date.available2025-12-05T13:55:10Z
dc.date.issued2022
dc.description.abstract<jats:p>Causality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.</jats:p>
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,5
dc.description.number2261
dc.description.points100
dc.description.volume478
dc.identifier.doi10.1098/rspa.2021.0835
dc.identifier.eissn1471-2946
dc.identifier.issn1364-5021
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/6252
dc.languageen
dc.relation.ispartofProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
dc.relation.pagesart. 20210835
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.encausality
dc.subject.encausal systems
dc.subject.enstochastics
dc.subject.enimpulse response function
dc.subject.ensystem identification
dc.titleRevisiting causality using stochastics: 1. Theory
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue2261
oaire.citation.volume478