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[[File:Diagram of Dynamic Causal Modelling - Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging by Karl Friston.png|thumb|300px|比较两个竞争的因果模型(DCM，GCM)用于解释[[fMRI 图像]]<ref>{{cite journal | doi=10.1371/journal.pbio.1000033 | pmid=19226186 | pmc=2642881 | author=Karl Friston | title=Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging | journal=PLOS Biology| volume=7 | number=2 | pages=e1000033 | date=Feb 2009}}</ref>]]
[[File:Diagram of Dynamic Causal Modelling - Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging by Karl Friston.png|thumb|300px|比较两个竞争的因果模型(DCM，GCM)用于解释[[fMRI 图像]]<ref>{{cite journal | doi=10.1371/journal.pbio.1000033 | pmid=19226186 | pmc=2642881 | author=Karl Friston | title=Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging | journal=PLOS Biology| volume=7 | number=2 | pages=e1000033 | date=Feb 2009}}</ref>]]
* {{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}}
* {{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>
*<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>
== 编者推荐==
===书籍推荐===
[[File:统计因果推理入门.jpg|200px|thumb|right|《统计因果推理入门》封面]]
*[[统计因果推理入门]] 对应英文[[Causal Inference in Statistics: A Primer]]
这本书非常适合初学者入门因果科学，这里面涉及到对结构因果模型的详细定义和阐述，非常清晰易懂。
*[[Counterfactuals and Causal Inference: Methods and Principles for Social Research]]
===课程推荐===
*[https://campus.swarma.org/course/1937 如何用信息视角理解现代因果模型框架？]
::这个视频内容来自[[集智俱乐部读书会]]-因果科学与Causal AI读书会第一季内容的分享，这个视频为大家串讲因果推理的相关论文，着眼与因果研究的源头，简单介绍哲学中的因果思考。其次重点是用因果之梯（她的信息视角--回答因果问题需要相应的信息）和一个例子，来理解现代因果建模框架；最后梳理因果推理和 AI 领域的融合，以及Causal AI 的强人工智能之路。
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