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Bayesian networks and causal diagrams

Summary:
Bayesian networks and causal diagrams Whereas a Bayesian network​ can only tell us how likely one event is, given that we observed another, causal diagrams can answer interventional and counterfactual questions. For example, the causal fork A C tells us in no uncertain terms that wiggling A would have no effect on C, no matter how intense the wiggle. On the other hand, a Bayesian network is not equipped to handle a ‘wiggle,’ or to tell the difference between seeing and doing, or indeed to distinguish a fork from a chain [A –> B –> C]. In other words, both a chain and a fork would predict that observed changes in A are associated with changes in C, making no prediction about the effect of ‘wiggling’ A. Advertisements

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Bayesian networks and causal diagrams

Bayesian networks and causal diagramsWhereas a Bayesian network​ can only tell us how likely one event is, given that we observed another, causal diagrams can answer interventional and counterfactual questions. For example, the causal fork A <– B –> C tells us in no uncertain terms that wiggling A would have no effect on C, no matter how intense the wiggle. On the other hand, a Bayesian network is not equipped to handle a ‘wiggle,’ or to tell the difference between seeing and doing, or indeed to distinguish a fork from a chain [A –> B –> C]. In other words, both a chain and a fork would predict that observed changes in A are associated with changes in C, making no prediction about the effect of ‘wiggling’ A.

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Lars Pålsson Syll
Professor at Malmö University. Primary research interest - the philosophy, history and methodology of economics.

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