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"The key assumption is that the pattern of noise in the cause will be different to the pattern of noise in the effect. That’s because any noise in X can have an
by mazsa 12y ago
"The key assumption is that the pattern of noise in the cause will be different to the pattern of noise in the effect. That’s because any noise in X can have an influence on Y but not vice versa."[...] "That’s a fascinating outcome. It means that statisticians have good reason to question the received wisdom that it is impossible to determine cause and effect from observational data alone." https://medium.com/the-physics-arxiv-blog/cause-and-effect-the-revolutionary-new-statistical-test-that-can-tease-them-apart-ed84a988e https://medium.com/the-physics-arxiv-blog/cause-and-effect-t...
- PeterWhittaker 12y agoI 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).
- kghose 12y agoDoes not rule out shared cause. You observe X and Y and find a correlation. You still have to consider that X and Y are caused by Z. Noise in Z will have echoes in both X and Y.
- cafebeen 12y agoExactly. They assume this isn't happening, so it's not a very useful idea in practice...
- dang 12y agoThanks, that's a good overview and we switched to it. https://news.ycombinator.com/item?id=8777258 https://news.ycombinator.com/item?id=8777258