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
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中級
完成時間大約為18 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
您將獲得的技能
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
中級
完成時間大約為18 小時
英語（English）
字幕：法語（French）, （歐洲人講的）葡萄牙語, 俄語（Russian）, 英語（English）, 西班牙語（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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 4 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 4 小時
8 個視頻 （總計 128 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）
完成時間為 5 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 5 小時
12 個視頻 （總計 171 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）
審閱
- 5 stars77.28%
- 4 stars18.48%
- 3 stars2.22%
- 2 stars0.89%
- 1 star1.11%
來自A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA的熱門評論
由 YZ 提供2021年12月14日
It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.
由 LL 提供2021年4月8日
The course is very simply explained, definitely a great introduction to the subject. There are some missing links, but minor compared to overall usefulness of the course.
由 PD 提供2018年7月14日
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 CR 提供2019年2月10日
I thought this was a good overview and I'm glad I took the course, but I would have preferred more hands on programming assignments.
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