更改

因果的不同概念涉及不变关系的概念。在识别手写数字的情况下，数字形状决定含义，因此形状和含义是不变量，改变形状会改变含义。其他属性则没有此性质（如颜色）。此不变性对于在各种非不变量所构成语境中生成的数据集都应满足。与其使用汇总的数据集进行学习评估因果关系，不如对一个数据集进行学习并对另一数据集进行测试，这可以帮助将变化属性与不变量区分开。<ref>{{Cite web|url=https://www.technologyreview.com/s/613502/deep-learning-could-reveal-why-the-world-works-the-way-it-does/|title=Deep learning could reveal why the world works the way it does|last=Hao|first=Karen|date=May 8, 2019|website=MIT Technology Review|language=en-US|access-date=February 10, 2020}}</ref>

因果的不同概念涉及不变关系的概念。在识别手写数字的情况下，数字形状决定含义，因此形状和含义是不变量，改变形状会改变含义。其他属性则没有此性质（如颜色）。此不变性对于在各种非不变量所构成语境中生成的数据集都应满足。与其使用汇总的数据集进行学习评估因果关系，不如对一个数据集进行学习并对另一数据集进行测试，这可以帮助将变化属性与不变量区分开。<ref>{{Cite web|url=https://www.technologyreview.com/s/613502/deep-learning-could-reveal-why-the-world-works-the-way-it-does/|title=Deep learning could reveal why the world works the way it does|last=Hao|first=Karen|date=May 8, 2019|website=MIT Technology Review|language=en-US|access-date=February 10, 2020}}</ref>

== See also ==

== 其他词条 ==

*[[Bayesian network#Causal networks|Causal network]] – a [[Bayesian network]] with an explicit requirement that the relationships be causal

*[[Bayesian network#Causal networks|Causal network]] – a [[Bayesian network]] with an explicit requirement that the relationships be causal