Results 11 to 20 of about 9,946,919 (251)
Non-causal explanation in science [PDF]
, 2013 Non-causal and causal explanation in science are unified under an extension of James Woodward's manipulationist account of causal explanation. Scientific explanation is about capturing and representing the modal structure of the world.
Pexton, Mark
core +6 more sources
Inferring causal phenotype networks using structural equation models
Genetics Selection Evolution, 2011 Phenotypic traits may exert causal effects between them. For example, on the one hand, high yield in dairy cows may increase the liability to certain diseases and, on the other hand, the incidence of a disease may affect yield negatively.
de los Campos Gustavo +5 more
doaj +1 more source
Genetics Selection Evolution, 2021 Background Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy.
Emhimad A. Abdalla +2 more
doaj +1 more source
Causal assessment in demographic research
Genus, 2020 Causation underlies both research and policy interventions. Causal inference in demography is however far from easy, and few causal claims are probably sustainable in this field. This paper targets the assessment of causality in demographic research.
Guillaume Wunsch, Catherine Gourbin
doaj +1 more source
Interventionist Counterfactuals on Causal Teams [PDF]
Electronic Proceedings in Theoretical Computer Science, 2019 We introduce an extension of team semantics which provides a framework for the logic of manipulationist theories of causation based on structural equation models, such as Woodward's and Pearl's; our causal teams incorporate (partial or total) information
Fausto Barbero, Gabriel Sandu
doaj +1 more source
Correlational data, causal hypotheses, and validity [PDF]
, 2010 A shared problem across the sciences is to make sense of correlational data coming from observations and/or from experiments. Arguably, this means establishing when correlations are causal and when they are not. This is an old problem in philosophy. This
Russo, Federica, Federica Russo
core +1 more source
Three methods that advance tests of complex psychological theory using structural equation models.
, 2023 Theories of psychological constructs like personality, pathology, or cognition, give rise to models that implicitly or explicitly impose a causal structure on mental processes.
Michel Guillaume Nivard +2 more
core +1 more source
FROM CAUSAL MODELS TO COUNTERFACTUAL STRUCTURES [PDF]
The Review of Symbolic Logic, 2012 AbstractGalles & Pearl (l998) claimed that “for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis’s [possible-worlds] framework.” This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis’s framework.
openaire +4 more sources
Quantifying Causal Path-Specific Importance in Structural Causal Model
Computation, 2023 Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a ...
Xiaoxiao Wang 0002 +5 more
openaire +3 more sources
Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions
Journal of Causal Inference, 2017 The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [1, 2] excludes disjunctive conditionals, such as “had X$X$ been x1 or x2$x_1~\mbox{or}~x_2$,” as well as disjunctive actions such as do(X=x1 or X=x2)$do(X=x_1 ...
Pearl Judea
doaj +1 more source