论文标题
核对因果关系
Reconciling Causality and Statistics
论文作者
论文摘要
自纪律初期以来，统计学家就警告我们，两种观察之间的实验相关性绝不意味着存在因果关系。关于观察数据中存在哪些线索的问题，这些问题可以告知我们这种因果关系的存在，这是如此合法。它实际上是任何科学努力的根源。几十年来，统计学家阐明因果关系的唯一接受方法就是所谓的随机对照试验。除了这个臭名昭著的例外，因果关系问题在很大程度上仍然是许多人的禁忌。这种情况的原因之一是缺乏适当的数学框架来以明确的方式提出此类问题。幸运的是，随着犹大珍珠和同事发起的所谓因果关系革命的出现，过去几年改变了这些几年。这篇教学论文的目的是以紧凑，独立的方式以具体的商业示例作为插图来介绍他们的思想和方法。
Statisticians have warned us since the early days of their discipline that experimental correlation between two observations by no means implies the existence of a causal relation. The question about what clues exist in observational data that could informs us about the existence of such causal relations is nevertheless more that legitimate. It lies actually at the root of any scientific endeavor. For decades however the only accepted method among statisticians to elucidate causal relationships was the so called Randomized Controlled Trial. Besides this notorious exception causality questions remained largely taboo for many. One reason for this state of affairs was the lack of an appropriate mathematical framework to formulate such questions in an unambiguous way. Fortunately thinks have changed these last years with the advent of the so called Causality Revolution initiated by Judea Pearl and coworkers. The aim of this pedagogical paper is to present their ideas and methods in a compact and self-contained fashion with concrete business examples as illustrations.
