Distinguishing cause from effect using observational data: methods and benchmarks · HackerPapers discussions

Distinguishing cause from effect using observational data: methods and benchmarks — mentioned by tankenmate

Sun, 27 May 2018 18:32:12 GMT

One way to test this is with data that has sufficient random walks (say for example daily open and close prices over several decades) and use the additive noise method (ANM)[0] to determine if noise from one correlator affects noise in the other (and hence one causes the other). https://arxiv.org/abs/1412.3773

Paper and discussion history. A mention is not an endorsement.

Distinguishing cause from effect using observational data: methods and benchmarks — mentioned by gwern

Tue, 05 Jul 2016 18:49:29 GMT

https://arxiv.org/abs/1412.3773 https://medium.com/the-physics-arxiv-blog/cause-and-effect-t...

Paper and discussion history. A mention is not an endorsement.

Distinguishing cause from effect using observational data: methods and benchmarks — mentioned by unimpressive

Tue, 05 Jan 2016 05:28:52 GMT

Link to paper: http://arxiv.org/pdf/1412.3773v3.pdf

Paper and discussion history. A mention is not an endorsement.

Distinguishing cause from effect using observational data: methods and benchmarks — mentioned by RoboTeddy

Sat, 20 Dec 2014 20:32:18 GMT

From the paper (http://arxiv.org/abs/1412.3773), > Concluding, our results provide evidence that distinguishing cause from effect is indeed possible by exploiting certain statistical patterns in the observational data. However, the performance of current state-of-the-art bivariate causal methods still has to be improved further in order to enable practical applications.

Paper and discussion history. A mention is not an endorsement.

Distinguishing cause from effect using observational data: methods and benchmarks — mentioned by dang

Sat, 20 Dec 2014 17:48:55 GMT

We changed the URL from http://arxiv.org/pdf/1412.3773v1.pdf because, with some exceptions (such as computing), HN tends to prefer the highest-quality general-interest article on a topic with the paper linked in comments. This comes up often enough that it is a good case for linking related URLs together, which is something we intend to work on in the new year.

Paper and discussion history. A mention is not an endorsement.

Distinguishing cause from effect using observational data: methods and benchmarks — HN submission

Wed, 17 Dec 2014 19:17:47 GMT

Distinguishing cause from effect using observational data

8 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.