by 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
Topics:
Lars Pålsson Syll considers the following as important: Theory of Science & Methodology
This could be interesting, too:
Lars Pålsson Syll writes Randomization and causal claims
Lars Pålsson Syll writes Race and sex as causes
Lars Pålsson Syll writes Randomization — a philosophical device gone astray
Lars Pålsson Syll writes Keynes on the importance of ‘causal spread’
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 <– 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.