Propensity score matching - A Crash Course in Causality: Inferring Causal Effects from

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Propensity score matching - A Crash Course in Causality: Inferring Causal Effects from Observational Data
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!
Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment).
At the end of the course, learners should be able to:
1. Define causal effects using potential outcomes
2. Describe the difference between association and causation
3. Express assumptions with causal graphs
4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)
5. Identify which causal assumptions are necessary for each type of statistical method
So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!
Instrumental Variable, Propensity Score Matching, Causal Inference, Causality
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.,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!
Propensity score matching - A Crash Course in Causality: Inferring Causal Effects from Observational Data
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