Studies can always have confounding factors, of course. And I wrote “falsification” but could have more accurately said something about reducing the posterior probability. Lack of correlation (e.g. with speed) would sharply reduce the p.p. of a simple model with one input (e.g. gas pedal), but only reduce the p.p. of a model with multiple inputs (e.g. gas pedal + hilly terrain) to a weaker extent.
Studies can always have confounding factors, of course. And I wrote “falsification” but could have more accurately said something about reducing the posterior probability. Lack of correlation (e.g. with speed) would sharply reduce the p.p. of a simple model with one input (e.g. gas pedal), but only reduce the p.p. of a model with multiple inputs (e.g. gas pedal + hilly terrain) to a weaker extent.
By the way, you can still learn structure from data in the presence of unobserved confounders. The problem becomes very interesting indeed, then.
Oh, awesome. Can you provide a link / reference / name of what I should Google?
http://www.hss.cmu.edu/philosophy/spirtes/n-oracle.ps
(FCI algorithm)
http://www.cs.huji.ac.il/~nir/Abstracts/Fr2.html
(structural EM)
http://www.stat.washington.edu/tsr/uai-causal-structure-learning-workshop/
(look for “parameter and structure learning in nested Markov models”)