因果对因果关系的区分(CauseEffectPairs Distinguishing between cause and effect)_机器学习_科研数据集

因果对:因果关系的区分(CauseEffectPairs:

Distinguishing between cause and effect)

数据介绍：

The data set consists of 8 N x 2 matrices, each representing a cause-effect pair and the task is to identify which variable is the cause and which one the effect.

The origin of the data is hidden for the participants but known to the organizers. The data sets are chosen such that we expect common agreement on which one is the cause and which one the effect. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect-relation.

关键词：

因果推论,因果关系对,识别,关系,统计依赖, pairwise causal inference,cause-effect pair,identify,relation,statistical dependences,

数据格式：

TEXT

数据详细介绍：

CauseEffectPairs: Distinguishing between cause and effect

Contact: Dominik Janzing - Submitted: 2010-05-04 13:53 - Views : 1587 - [Edit entry]

?Authors: Joris Mooij, Dominik Janzing, Bernhard Sch?lkopf

?Key facts: The goal of this task is to distinguish between cause and effect.

Detailed description

For a given N x 2 matrix (containing N samples of 2 continuous variables) where one variable is known to causally influence the other, but not vice versa, the task is to identify which variable is the cause and which one the effect.

Data origin

For this task, the origin of the data is hidden for the participants but known to the organizers. The data sets are chosen such that we expect common agreement on which one is the cause and which one the effect. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect-relation. ?Keywords: pairwise causal inference

?Download BibTeX

?Download the dataset

?Dataset mirror

?post challenge update: more data

Abstract:

The data set consists of 8 N x 2 matrices, each representing a cause-effect pair and the task is to identify which variable is the cause and which one the effect.

The origin of the data is hidden for the participants but known to the organizers. The data sets are chosen such that we expect common agreement on which one is the cause and which one the effect. Even though part of the statistical dependences may also be due to hidden common causes, common sense tells us that there is a significant cause-effect-relation.

数据预览：

点此下载完整数据集