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!
ofrecido por
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Universidad de PensilvaniaAcerca de este Curso
Habilidades que obtendrás
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
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Universidad de Pensilvania
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.
Programa - Qué aprenderás en este curso
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.
Reseñas
- 5 stars77,25 %
- 4 stars18,46 %
- 3 stars2,25 %
- 2 stars0,90 %
- 1 star1,12 %
Principales reseñas sobre A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.
This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.
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.
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.
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