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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中级
完成时间大约为13 小时
英语（English）
字幕：英语（English）
您将获得的技能
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
学生职业成果
50%
完成这些课程后已开始新的职业生涯
23%
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13 小时
英语（English）
字幕：英语（English）
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 3 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 3 小时
8 个视频 （总计 128 分钟）
8 个视频
Confusion over causality19分钟
Potential outcomes and counterfactuals13分钟
Hypothetical interventions17分钟
Causal effects19分钟
Causal assumptions18分钟
Stratification23分钟
Incident user and active comparator designs14分钟
3 个练习
Practice Quiz4分钟
Practice Quiz4分钟
Causal effects18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）
8 个视频
Confounding6分钟
Causal graphs9分钟
Relationship between DAGs and probability distributions15分钟
Paths and associations7分钟
Conditional independence (d-separation)13分钟
Confounding revisited9分钟
Backdoor path criterion15分钟
Disjunctive cause criterion9分钟
2 个练习
Practice Quiz8分钟
Identify from DAGs sufficient sets of confounders22分钟
完成时间为 4 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 4 小时
12 个视频 （总计 171 分钟）
12 个视频
Overview of matching12分钟
Matching directly on confounders13分钟
Greedy (nearest-neighbor) matching17分钟
Optimal matching10分钟
Assessing balance11分钟
Analyzing data after matching20分钟
Sensitivity analysis10分钟
Data example in R16分钟
Propensity scores11分钟
Propensity score matching14分钟
Propensity score matching in R15分钟
5 个练习
Practice Quiz6分钟
Practice Quiz8分钟
Matching12分钟
Propensity score matching10分钟
Data analysis project - analyze data in R using propensity score matching16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）
9 个视频
More intuition for IPTW estimation9分钟
Marginal structural models11分钟
IPTW estimation11分钟
Assessing balance9分钟
Distribution of weights9分钟
Remedies for large weights13分钟
Doubly robust estimators15分钟
Data example in R26分钟
3 个练习
Practice Quiz6分钟
IPTW18分钟
Data analysis project - carry out an IPTW causal analysis8分钟
审阅
来自A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA的热门评论
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.
由 OE 提供Jul 29, 2020
I enjoyed the course a lot and I think I took a lot from it as well. The quizzes and computer projects were appropriate, and the resourcees posted were very useful.
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