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I don't think it's hype or over stating things to suggest that this may be the most significant advance in practical statistics and methodologies for scientific
by PeterWhittaker 12y ago
I don't think it's hype or over stating things to suggest that this may be the most significant advance in practical statistics and methodologies for scientific investigation in years, perhaps decades.
Like many brilliant ideas, it seems so obvious in retrospect, another great "Why didn't I think of that?" moment.
- nabla9 12y agoCausal discovery has been research subject in statistics and machine learning for years. See, for example: Causation, Prediction, and Search by Spirtes, Glymour and Scheines (1993) https://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/photoz/.g/scottd/fullbook.pdf https://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/p... Computation, Causation, & Discovery (1999), edited by C. Glymour and G. Cooper, MIT Press
- deleted 12y ago[deleted]
- jacobn 12y ago"We simplify the causal discovery problem by assuming no confounding, selection bias and feedback." That's a pretty big caveat. Not saying the paper is not important, just that it's not (yet?) a full solution to the overall conundrum.
- UhUhUhUh 12y agoParticularly since the confounding issue is really enormous in science. And sits at the core of the example the article gives in introduction... That would be an achievement in itself to build an experiment without confounder(s).