[R] Your pardon: An article possibly of interest to statisticians

ce zadig_1 at excite.com
Fri Dec 19 17:22:46 CET 2014

Nice but as this is R forum, is there an R package related to it ?

-----Original Message-----
From: "John McKown" [john.archie.mckown at gmail.com]
Date: 12/18/2014 01:38 PM
To: "r-help" <r-help at r-project.org>
Subject: [R] Your pardon: An article possibly of interest to statisticians

I do hope this doesn't upset anyone. But it appears rather interesting to
me, despite the fact that I'm not a statistician. So I thought that it
might be of interest to some others here.



Title: Distinguishing cause from effect using observational data: methods
and benchmarks
The discovery of causal relationships from purely observational data is a
fundamental problem in science. The most elementary form of such a causal
discovery problem is to decide whether X causes Y or, alternatively, Y
causes X, given joint observations of two variables X, Y . This was often
considered to be impossible. Nevertheless, several approaches for
addressing this bivariate causal discovery problem were proposed recently.
In this paper, we present the benchmark data set CauseEffectPairs that
consists of 88 different "cause-effect pairs" selected from 31 datasets
from various domains. We evaluated the performance of several bivariate
causal discovery methods on these real-world benchmark data and on
artificially simulated data. Our empirical results provide evidence that
additive-noise methods are indeed able to distinguish cause from effect
using only purely observational data. In addition, we prove consistency of
the additive-noise method proposed by Hoyer et al. (2009).

Returning to lurkerdom.

While a transcendent vocabulary is laudable, one must be eternally careful
so that the calculated objective of communication does not become ensconced
in obscurity.  In other words, eschew obfuscation.

111,111,111 x 111,111,111 = 12,345,678,987,654,321

Maranatha! <><
John McKown

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