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!
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A Crash Course in Causality: Inferring Causal Effects from Observational Data
宾夕法尼亚大学課程信息
您將獲得的技能
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
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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
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.
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.
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.
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.
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- 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
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.
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.
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.
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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