The full course title was - deep breath - The Philosophical Foundations of Statistical Modeling and Causal Inference. It was a economics professor and a visiting philosophy professor teaching their research.

And it turns out I still have the syllabus lying around, so I don't need to try to explain it myself: http://www.cs.vt.edu/~scschnei/syllabus.pdf

Thats great stuff! thanks for sharing. Any hindsigths? something rigth out of your head about the subject???

Sad to say, my only real takeaway lessons were:

- All statistical models have assumptions. Even if a model looks like it fits the data, make sure the data doesn't violate those assumptions. If it does, the model doesn't fit.

- Causation can be inferred, with confidence, just by analyzing data.

Honestly, the econometrics stuff was presented poorly. What looked like pages from a book were put up on the projector (and in some cases, I think they were book pages), and the professor would just talk through the page. Picking up anything worthwhile from his lectures was hard - he knew the class had a varied background (some CS, some philosophy, some economics, even one person from marketing), but he still went faster than my prob/stat background could keep up.

The causal inference stuff was presented better, but I think the subject matter is more intuitive in general. His (the philosophy professor) math was graph theory, which I have a firmer grasp of.

More along the lines of philosophy of science then; great stuff!!!