# Reichenbach’s Common Cause Principle

> 【EN】The Common Cause Principle was introduced by Hans Reichenbach, in The Direction of Time , which was published posthumously in 1956. Suppose that two events A and B are positively correlated: \(p(A\cap B)>p(A)p(B)\). Suppose, moreover, that neither event is a cause of the other. Then, Reichenbach’s Common Cause Principle (RCCP) states that A and B will have a common cause that renders them conditionally independent. Reichenbach incorporated his RCCP into a new probablistic theory of causation, and used it to describe a (purported) macrostatistical temporal asymmetry in analogy with the second law of thermodynamics. The principle is significant because it posits a connection between causal structure and probabilistic correlations, thus facilitating causal inference from observed correlations. However, RCCP has been controversial, and a number of counterexamples have been proposed. … 【中】该原则由赖兴巴赫在其身后出版的《时间的方向》中提出，后被纳入他的概率因果理论，并用来描述一种宏观统计上的时间不对称，类比热力学第二定律。词条考察其历史背景、若干示例与所谓的反例、叉状不对称，以及因果马尔可夫条件，最后讨论量子场论中共同因原则的命运。

- ID: m13278
- Category: technical
- Domain: 科学方法论

## Definition

赖兴巴赫的共同因原则说：若两事件 A 与 B 正相关，即两者共同发生的概率大于各自概率之积，且互不为因，那么必存在一个共同因，使得在给定该共同因时 A 与 B 条件独立。 脚手架作用：- 相关归因：见两事相关且互不为因时，先假设共同因再做条件化检验。 - 混杂识别：以条件独立为判据，找出数据中隐藏的第三变量。

## How it works

相关的两个事件若互不为因，其相关性就只能来自结构：要么有共同因在前把它们筛分出来，使条件化之后相关消失；要么存在其他因果联系。共同因因此成为对相关性最经济的解释，也是概率因果推断与因果图方法的基石。

## Practice

1) 确认两个事件确实存在统计相关；2) 排除两者之间的直接因果联系；3) 寻找能同时影响两者的候选共同因；4) 检验条件化后相关是否消失；5) 若消失不了，考虑其他结构或承认这是反例。

## Use

- 相关归因：见两事相关且互不为因时，先假设共同因再做条件化检验。 - 混杂识别：以条件独立为判据，找出数据中隐藏的第三变量。

[Read the web page](https://thinkingmodels.site/en/entries/detail/m13278)