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Causal Reasoning with Causal Models
2022We introduce and discuss the use of Bayesian networks for causal modeling. Despite their growing popularity and utility in this application, numerous objections to it have been raised. We address the claims that Chickering's arc reversal rule undermines a causal interpretation and that failures of Reichenbach's Common Cause Principle, or again failures
Korb, K B +3 more
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Knowledge Acquisition, 1989
We describe an approach for understanding devices which provides leverage for the knowledge acquisition taks. In our functional approach, knowledge is acquired in three steps: the device is decomposed to an ensemble of subdevices, the functions/goals/purposes of each subdevice are stated abstractly, and finally, the way(s) each function/goal/purpose is
Jon Sticklen +2 more
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We describe an approach for understanding devices which provides leverage for the knowledge acquisition taks. In our functional approach, knowledge is acquired in three steps: the device is decomposed to an ensemble of subdevices, the functions/goals/purposes of each subdevice are stated abstractly, and finally, the way(s) each function/goal/purpose is
Jon Sticklen +2 more
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The Development of Causal Reasoning
, 2017 Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development.
Elizabeth Bonawitz, Paul Muentener
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The development of causal reasoning
WIREs Cognitive Science, 2012AbstractHow do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference ...
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Cognitive Science, 2016
AbstractThis article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category‐based judgments as well as a number of other cognitive phenomena,CGMs are only able to represent causal structures that are acyclic.
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AbstractThis article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category‐based judgments as well as a number of other cognitive phenomena,CGMs are only able to represent causal structures that are acyclic.
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Causal Reasoning in Econometric Models
Decision Support Systems, 1995Abstract Propagation of change based on causal ordering is a central element of causal reasoning in economic models. While causal reasoning has most often been applied in qualitative models, we demonstrate a technique for causal reasoning that offers explanations of structure and behaviour in quantitative, econometric contexts.
Arthur M. Farley
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The Oxford Handbook of Causal Reasoning
, 2017 Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about
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1999
The objective of this paper is to study causal inference as an issue of common sense reasoning. It is well known that causal inference cannot be represented by classical implication which has a number of properties not correct for causal implication, such as monotonicity, contraposition or transitivity.
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The objective of this paper is to study causal inference as an issue of common sense reasoning. It is well known that causal inference cannot be represented by classical implication which has a number of properties not correct for causal implication, such as monotonicity, contraposition or transitivity.
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Causal reasoning in systems modelling
International Journal of Systems Science, 1998This paper will not discuss the philosophy of causality in depth, but will focus on its applications to the modelling of macroeconomic systems. There has been much discussion in econometrics about the extraction of structural parameters. This is the question of ‘identification’, and there are well-established procedures for this extraction.
Y. K. Wong +3 more
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