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
Пенсильванский университетОб этом курсе
Приобретаемые навыки
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
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Пенсильванский университет
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.
Программа курса: что вы изучите
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.
Рецензии
- 5 stars76,85 %
- 4 stars18,99 %
- 3 stars2,18 %
- 2 stars0,87 %
- 1 star1,09 %
Лучшие отзывы о курсе A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
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.
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.
I completed all 4 available courses in causal inference on Coursera. This one has the best teaching quality. The material is very clear and self-contained!
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.
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