That's a very interesting speech Peter - thanks for pointing it out. I think it backs up the following argument, and I'd be grateful for any opinions and discussion.
1. Structural Equation Models (involving equations of the form E=f(C,e) where E is an effect, f is a function, C are the direct causes of E and e is an error term) are used in econometrics and the social sciences for forecasting and as decision support tools. 2. These models tend to make very strong assumptions. E.g. all relevant variables have been identified; the qualitative causal structure has been correctly identified; variables only depend on those variable that are perceived to be their direct causes; f is linear; the error terms are independent and normally distributed. 3. Consequently these models are rarely reliable or robust. 4. Consequently it would be better to use causal Bayesian networks instead, since in that case we only need to estimate the probability distribution of each variable conditional on its direct causes. Such models should be checked (one should check that the causal Markov condition holds for the model and that the model is robust for forcasting and modeling interventions) and refined as necessary. 5. Only when we are confident that a causal Bayesian network has captured the correct qualitative causal structure would it be fruitful to investigate functional relationships between cause and effect. 6. Slogan: "causal net before functional model". Any comments? Jon ------------------- Jon Williamson Department of Philosophy, King's College, Strand, London, WC2R 2LS, UK http://www.kcl.ac.uk/jw ----- Original Message ----- From: "Peter McBurney" <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Sent: Wednesday, September 10, 2003 7:00 PM Subject: [UAI] Monetary Policy under Uncertainty > Hi all -- > > You may be interested in this recent speech by Alan Greenspan, Chairman > of the US Federal Reserve Board, on "Monetary Policy under Uncertainty": > > http://www.federalreserve.gov/boarddocs/speeches/2003/20030829/ > > He gives as good an account as I've seen on the failure of classical > decision-theory models to meet the needs of real-world decision-makers. > > > -- Peter
