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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 ==
*[[Bayesian network#Causal networks|Causal network]] – a [[Bayesian network]] with an explicit requirement that the relationships be causal
*[[Structural equation modeling]] – a statistical technique for testing and estimating causal relations
* [[Path analysis (statistics)]]
*[[Bayesian network]]
*[[Causal map]]
==References==
{{Reflist}}
== Sources ==
* {{Cite book|url={{google books |plainurl=y |id=LLkhAwAAQBAJ}}|title=Causality|last=Pearl|first=Judea|date=2009-09-14|publisher=Cambridge University Press|isbn=9781139643986|language=en}}
== External links ==
*{{Cite journal|last=Pearl|first=Judea|date=2010-02-26|title=An Introduction to Causal Inference|journal=The International Journal of Biostatistics|volume=6|issue=2|pages=Article 7|doi=10.2202/1557-4679.1203|issn=1557-4679|pmc=2836213|pmid=20305706}}
* {{PhilPapers|category|causal-modeling|Causal modeling}}
* {{Cite news|url=https://www.quantamagazine.org/how-artificial-intelligence-is-changing-science-20190311/|title=AI Algorithms Are Now Shockingly Good at Doing Science|last=Falk|first=Dan|date=2019-03-17|work=Wired|access-date=2019-03-20|issn=1059-1028}}
* {{Cite web|url=https://bostonreview.net/science-nature/tim-maudlin-why-world|title=The Why of the World|last=Maudlin|first=Tim|date=2019-08-30|website=Boston Review|language=en|access-date=2019-09-09}}
* {{Cite web|url=https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/|title=To Build Truly Intelligent Machines, Teach Them Cause and Effect|last=Hartnett|first=Kevin|website=Quanta Magazine|access-date=2019-09-19}}
*<ref>{{Citation|publisher=ICLR|title=Learning Representations using Causal Invariance|date=February 2020 |url=https://www.facebook.com/iclr.cc/videos/534780673594799|language=en|access-date=2020-02-10}}</ref>
{{DEFAULTSORT:Causal Model}}
[[Category:Causal diagrams]]
[[Category:Causality]]
[[Category:Formal epistemology]]
[[Category:Scientific modeling]]