
Causal inference with Bayes rule
The concept of causality has a controversial history. The question of wh...
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The DoCalculus Revisited
The docalculus was developed in 1995 to facilitate the identification o...
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Causality without potential outcomes and the dynamic approach
Several approaches to causal inference from observational studies have b...
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A Potential Outcomes Calculus for Identifying Conditional PathSpecific Effects
The docalculus is a wellknown deductive system for deriving connection...
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Replacing the docalculus with Bayes rule
The concept of causality has a controversial history. The question of wh...
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Evidencebased Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm
Evidencebased Prescriptive Analytics (EbPA) is necessary to determine o...
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On the Testable Implications of Causal Models with Hidden Variables
The validity OF a causal model can be tested ONLY IF the model imposes c...
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Multivariate Counterfactual Systems And Causal Graphical Models
Among Judea Pearl's many contributions to Causality and Statistics, the graphical dseparation criterion, the docalculus and the mediation formula stand out. In this chapter we show that dseparation provides direct insight into an earlier causal model originally described in terms of potential outcomes and event trees. In turn, the resulting synthesis leads to a simplification of the docalculus that clarifies and separates the underlying concepts, and a simple counterfactual formulation of a complete identification algorithm in causal models with hidden variables.
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