This delightful example of an academic hissy fit is from almost a year ago, but still has the power to shock in its lack of professionalism. I hope it shatters the all-too-common stereotype amongst some people of academics as calm intellectuals who want nothing more than to hear each other’s honestly help views. Phooey to that.
The topic of discussion (if it can be called that when the level of aggression is this high) is Phil Dawid’s article "Beware of the DAG", which makes very reasonable points about what possible causal inferences can be undertaken for different levels of "causal" assumptions on a Directed Acyclic Graph (which is more commonly known as a Bayesian Network when reflecting conditional independence properties, as is the case here), and discusses how reasonable and testable these "causal" assumptions are. In particular, he dismisses the popular but mostly deluded endeavour of "causal discovery", which is at once ill-posed and, so far, in my opinion, ill-answered. This hits right at what Clark Glymour is trying to sell, though. But that still doesn’t quite explain what caused this reaction…
NB. No-one from the mailing list replied to this directly, as far as I can tell. The archive for that month is available here.
And now, enjoy, typos included:
From: Clark Glymour <cg09>
Date: Thu, Apr 23, 2009 at 01:12
Subject: [Causality-ML] Professor Dawid’s paper
To: causality-ml
Professor Dawid ‘s worry is announced in his abstract:
“My fundamental concern is the relationship between, on the one hand, properties or concepts relating to an external reality, such as probabilistic independence or causality,which we wish to elucidate or manipulate; and, on the other hand, formal representations of such properties by means of mathematical or logical structures, such as graphs. It is important to avoid confusing the picture with the reality.”
Be at ease. Absolutely not to worry. I have never once, not once, seen someone draw a graph or write a formula when they actually thought they were manipulating what the symbols were supposed to denote. Not once. Word and object, we are ace at distinguishing those. So I thought, having solved Professor Dawid’s concern, I should stop, but I read on a little ways.
To a really important announcement:
“it is … worthy of continual repetition and emphasis, that there is absolutely no logical reason for there to be any connexion whatsoever between observations made under the different regimes of seeing and doing: a system may very well behave entirely differently when it is kicked than when it is left alone”
Good point that. I checked my logic books, no proofs of that connection. Also no proofs that the past ever was, no proofs that the future will come to be, no proofs that Professor Dawid has mental states, no proofs that an external world exists, no proofs that the so-called laws of nature will hold next week. Not much use, those logic books, unless you assume or hypothesize stuff and then want to know the consequences.
On the other hand, there is this funny literature—I wonder if Professor Dawid has read it—where people investigate when, under what various assumptions about the world, other things follow. Like, for example, there is this subject called Euclidean geometry where assumptions are make about space, and then all kinds of interesting other things are proved about space, really amazing stuff—you could use it to design buildings even. But I read that the assumptions are not always true. Pity. Also, there was this guy Newton who had these three assumptions, and then some “rules of reasoning” at the back. He got these amazing consequences, which mostly turned out to be correct, although I hear that his assumptions don’t always hold, and I sure could not find his rules of reasoning in my logic book.
I guess it can’t be the same with observing and doing. There just couldn’t be any assumptions about the connections and proofs from assumptions that you can make the kind of inferences Professor Dawid is talking about—inferences from observations to effects of actions. Or proofs that under other assumptions you can’t. Couldn’t be. So, nah…
In fact, Professor Dawid is really helpful about this. He tells us that if we make the wrong assumptions, or not enough of the right ones, we won’t get that logical connection between seeing and doing. Not a chance of it.
“We say that a DAG D with node-set V, a set of variables, represents a collection C of CIproperties over V if the relation (bunch of symbols here, way over my head) is in C if and only if S and T are d-separated by U in D. This relationship between a D and a collection of CI properties will constitute our semantic interpretation of a DAG.”
Well sure enough, one thing talks about causality, the other talks just about probability. Different terms. My logic book tells me there have to be terms in common between the premises and the conclusion—unless the conclusion is logically true. Kind of like “force” and “acceleration” or like “probability” and “unbiased,” or “perpetual” and “motion”–no logical connection. So Professor Dawid really nailed that one.
Well, I should go on reading this stuff, like how we should just talk about probability because graph theory isn’t mathematics (so many silly people who thought they were doing mathematics) and how science is all about conditional independence not causation (I knew those physicists and chemists and epidemiologists had to be crazy talking about what does and doesn’t cause what), and I am sure he has discovered a lot more stuff than those crazy causal guys who think they have methods that have discovered errors in a mass spectrometer aboard a satellite (imagine—they weren’t ever there), and how to tell what rocks are made of from the radiation bouncing off them, and how to reduce the rate of college dropouts in a college, and that acid rain caused plant die offs in an estuary, and the processes that go on in the brain in an experiment (something about fmri), and even global climate teleconnections—those guys are so crazy. But since I solved the problem Professor Dawid had at t!
he beginning, I will just have a martini.
Bye,
Clark Glymour
Bayesian Statistics Blog 