The first sentence "Statisticians have long thought it impossible to tell cause and effect apart using observational data" is very untrue. Economists, social scientists, and especially epidemologists have long used statistics to try to determine cause and effect from observational data. It is true that pure statisticians have largely avoided this question because it is very hard and, so far, only partial progress has been possible. They prefer problems they can solve completely. --Dan
On Dec 18, 2014, at 1:28 PM, Henry Baker <hbaker1@pipeline.com> wrote:
. . .
http://science.slashdot.org/story/14/12/18/1810221/cause-and-effect-how-a-re...
Statisticians have long thought it impossible to tell cause and effect apart using observational data. . . . Take for example, a correlation between wind speed and the rotation speed of a wind turbine. . . . That's a useful new trick in a statistician's armoury, particularly in areas of science where controlled experiments are expensive, unethical or practically impossible.
http://arxiv.org/abs/1412.3773
Distinguishing cause from effect using observational data: methods and benchmarks Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf (Submitted on 11 Dec 2014)
The discovery of causal relationships from purely observational data is a fundamental problem in science. . . . In addition, we prove consistency of the additive-noise method proposed by Hoyer et al. ( 2009).