You can call them “bayes nets” if the word “causal” seems too icky.
Ugh. A Bayesian network is not a causal model. I am going to have to exit this, I am finding having to explain the same things over and over again very frustrating :(. From what I could tell following this thread you subscribe to the notion that there is no difference between EDT and CDT. That’s fine, I guess, but it’s a very exotic view of decision theories, to put to mildly. It just seems like a bizarre face-saving maneuver on behalf of EDT.
I have a little bit of unsolicited advice (which I know is dangerous to do), please do not view this as a status play: read the bit of Pearl’s book where he discusses the difference between a Bayesian network, a causal Bayesian network, and a non-parametric structural equation model. This may also make it clear what the crucial difference between EDT and CDT is.
Ugh. A Bayesian network is not a causal model. I am going to have to exit this, I am finding having to explain the same things over and over again very frustrating :(. From what I could tell following this thread you subscribe to the notion that there is no difference between EDT and CDT. That’s fine, I guess, but it’s a very exotic view of decision theories, to put to mildly. It just seems like a bizarre face-saving maneuver on behalf of EDT.
I have a little bit of unsolicited advice (which I know is dangerous to do), please do not view this as a status play: read the bit of Pearl’s book where he discusses the difference between a Bayesian network, a causal Bayesian network, and a non-parametric structural equation model. This may also make it clear what the crucial difference between EDT and CDT is.
Also read this if you have time: www.biostat.harvard.edu/robins/publications/wp100.pdf
This paper discusses what a causal model is very clearly (actually it discusses 4 separate causal models arranged in a hierarchy of “strength.”)
EDT one-boxes on newcomb’s. Also I am well aware that not all bayes nets are causal models.