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!
提供方
A Crash Course in Causality: Inferring Causal Effects from Observational Data
宾夕法尼亚大学
課程信息
47,212 次近期查看
學生職業成果
50%
完成這些課程後已開始新的職業生涯
23%
通過此課程獲得實實在在的工作福利
您將獲得的技能
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
學生職業成果
50%
完成這些課程後已開始新的職業生涯
23%
通過此課程獲得實實在在的工作福利
可分享的證書
完成後獲得證書
100% 在線
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中級
完成時間大約為27 小時
建議：5 weeks of study, 3-5 hours per week
英語（English）
字幕：英語（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.
教學大綱 - 您將從這門課程中學到什麼
完成時間為 3 小時
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.
完成時間為 3 小時
8 個視頻 （總計 128 分鐘）
8 個視頻
Confusion over causality19分鐘
Potential outcomes and counterfactuals13分鐘
Hypothetical interventions17分鐘
Causal effects19分鐘
Causal assumptions18分鐘
Stratification23分鐘
Incident user and active comparator designs14分鐘
3 個練習
Practice Quiz4分鐘
Practice Quiz4分鐘
Causal effects18分鐘
完成時間為 2 小時
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.
完成時間為 2 小時
8 個視頻 （總計 86 分鐘）
8 個視頻
Confounding6分鐘
Causal graphs9分鐘
Relationship between DAGs and probability distributions15分鐘
Paths and associations7分鐘
Conditional independence (d-separation)13分鐘
Confounding revisited9分鐘
Backdoor path criterion15分鐘
Disjunctive cause criterion9分鐘
2 個練習
Practice Quiz8分鐘
Identify from DAGs sufficient sets of confounders22分鐘
完成時間為 4 小時
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.
完成時間為 4 小時
12 個視頻 （總計 171 分鐘）
12 個視頻
Overview of matching12分鐘
Matching directly on confounders13分鐘
Greedy (nearest-neighbor) matching17分鐘
Optimal matching10分鐘
Assessing balance11分鐘
Analyzing data after matching20分鐘
Sensitivity analysis10分鐘
Data example in R16分鐘
Propensity scores11分鐘
Propensity score matching14分鐘
Propensity score matching in R15分鐘
5 個練習
Practice Quiz6分鐘
Practice Quiz8分鐘
Matching12分鐘
Propensity score matching10分鐘
Data analysis project - analyze data in R using propensity score matching16分鐘
完成時間為 3 小時
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.
完成時間為 3 小時
9 個視頻 （總計 119 分鐘）
9 個視頻
More intuition for IPTW estimation9分鐘
Marginal structural models11分鐘
IPTW estimation11分鐘
Assessing balance9分鐘
Distribution of weights9分鐘
Remedies for large weights13分鐘
Doubly robust estimators15分鐘
Data example in R26分鐘
3 個練習
Practice Quiz6分鐘
IPTW18分鐘
Data analysis project - carry out an IPTW causal analysis8分鐘
審閱
4.7
來自A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA的熱門評論
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 AP 提供Dec 14, 2018
very good content. Story line is highly concise. However, Lecturer could be more stream-lined the the way of explaining. He sure is a skilled guy, however.
由 PD 提供Jul 14, 2018
Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.
由 CE 提供Jul 15, 2017
Works best on double speed (from settings menu of each video). Content is delivered in clear and relatable manner using interesting real world examples.
由 WF 提供Nov 24, 2018
This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.
由 AA 提供May 15, 2018
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
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