A simple constraint-based algorithm for efficiently mining observational databases for causal relationships
A simple constraint-based algorithm for efficiently mining observational databases for causal relationships
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DOI：
10.1023/a:1009787925236
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发表时间：
1997-01-01
影响因子：
4.8
通讯作者：
Cooper, GF
中科院分区：
文献类型：
--
作者：
Cooper, GF
本文提出了一种简单、高效的基于计算机的方法，用于从包含观测数据的数据库中发现因果关系。与实验数据相比，观测数据是被动观察的。大多数可用于数据挖掘的数据库都是观察性的。挖掘此类数据库以发现因果关系具有巨大的潜力。我们说明了观测数据如何限制测量变量之间的因果关系，有时甚至可以得出结论：一个变量正在导致另一个变量。这里的演示基于基于约束的因果发现方法。本文的主要目的是以尽可能简单的方式提出基于约束的因果发现方法，以便（1）轻松传达更复杂的基于约束的因果发现技术的基本思想，以及（2）允许感兴趣的读者快速编程并将该方法应用到他们自己的数据库中，作为使用更复杂的因果发现算法的开始。
This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.