We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!
A Crash Course in Causality: Inferring Causal Effects from Observational Data
宾夕法尼亚大学课程信息
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学生职业成果
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完成这些课程后已开始新的职业生涯
24%
通过此课程获得实实在在的工作福利
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中级
完成时间大约为18 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
学生职业成果
33%
完成这些课程后已开始新的职业生涯
24%
通过此课程获得实实在在的工作福利
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为18 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
宾夕法尼亚大学
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
教学大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
Welcome and Introduction to Causal Effects
This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
完成时间为 4 小时
8 个视频 （总计 128 分钟）
完成时间为 2 小时
Confounding and Directed Acyclic Graphs (DAGs)
This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
完成时间为 2 小时
8 个视频 （总计 86 分钟）
完成时间为 5 小时
Matching and Propensity Scores
An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
完成时间为 5 小时
12 个视频 （总计 171 分钟）
完成时间为 3 小时
Inverse Probability of Treatment Weighting (IPTW)
Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
完成时间为 3 小时
9 个视频 （总计 119 分钟）
审阅
来自A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA的热门评论
由 JC 提供Nov 20, 2020
A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.
由 WL 提供Mar 16, 2019
Very easy to follow examples and great coverage for such an important topic! The delivery sometimes get repetitive and I wish we talked more about how the uncertainties are derived.
由 MF 提供Dec 27, 2017
I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.
由 FF 提供Nov 29, 2017
The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.
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