Ch1 因果理论相关概念

By reading this book you are expressing an interest in learning about causal inference. 但是，作为一个人，您早就已经掌握了因果推理的基本概念。 您当然知道什么是因果关系；您清楚了解关联和因果关系之间的区别；并且您一生中都在不断使用这些知识。实际上，如果您不理解这些因果概念，那么您将无法生存足够长的时间来阅读本章，甚至无法学会阅读。 As a toddler you would have jumped right into the swimming pool after observing that those who did so were later able to reach the jam jar. As a teenager, you would have skied down the most dangerous slopes after observing that those who did so were more likely to win the next ski race. As a parent, you would have refused to give antibiotics to your sick child after observing that those children who took their medicines were less likely to be playing in the park the next day.

Since you already understand the definition of causal effect and the difference between association and causation, 因此不要期望从本章获得深入的概念见解。相反，本章的目的是介绍数学符号，以形式化您已经拥有的因果直觉。 确保您可以将因果直觉与此处介绍的数学符号匹配。该符号对于精确定义因果概念是必要的，我们将在整本书中使用它。

知识要点

本章是关于因果效应的基本概念的定义。

  • Individual causal effects cannot be identified, that is, cannot be expressed as a function of the observed data, because of missing data. So we want to estimate the average cause effects of the population.

  • There are 3 ways of measuring causal effect, which is the causal risk difference, risk ratio, and odds ratio.

  • Sampling variability as a source of random error.

更多阅读

Importantly, this is not a philosophy book. We remain agnostic about metaphysical concepts like causality and cause. 哲学上因果相关内容 mental causation, consciousness 等请参见 Free will, Causality and Neuroscience

Judea Pearl 是 Causal AI 的奠基人，Bernhard Scholkopf 推进了 Causality for Machine Learning，Yoshua Bengio 提出了 System 2 deep learning 作为 Causal AI 的一个范式。个人沉醉于 life and Intelligence 之美，众多工具中（包括数学，计算机，物理，复杂系统等等），个人偏好用信息论视角研究如何教会机器因果思维，希望创造具备 free will 的 AI，使之成为我们的良师益友，一起探索解密生命和智能的终极奥秘。该书对因果推理的概念和方法进行了全面的介绍。是我们 Causal AI 中组成内容。另外两个与之密切相关的项目是：