Results 11 to 20 of about 644,547 (283)
Review of Causal Discovery Methods Based on Graphical Models
Frontiers in Genetics, 2019 A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even ...
Clark Glymour, Kun Zhang, Peter Spirtes
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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
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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
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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
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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
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Does modeling causal relationships improve the accuracy of predicting lactation milk yields?
JDS Communications, 2023 This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields.
Xiao-Lin Wu +7 more
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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.
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Learning Latent Structural Causal Models
, 2022 Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given.
Subramanian, Jithendaraa +7 more
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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
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Invariant Causal Prediction for Nonlinear Models
Journal of Causal Inference, 2018 An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system’s underlying causal structure.
Heinze-Deml Christina +2 more
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