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!
4.7
74 Ratings
Free
per course
Incentives
Shareable Certificate
100% online
Flexible deadlines
Intermediate Level
Approx. 30 hours to complete
English
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FAQs About "A Crash Course in Causality: Inferring Causal Effects from Observational Data"
Is the online course "A Crash Course in Causality: Inferring Causal Effects from Observational Data" free?
Yes, "A Crash Course in Causality: Inferring Causal Effects from Observational Data" is a free online course offered on the online classes platform Coursera
Is "A Crash Course in Causality: Inferring Causal Effects from Observational Data" an online course worth taking?
71 students from the online classes platform Coursera have given an average rating of 4.7 to the online "A Crash Course in Causality: Inferring Causal Effects from Observational Data" and at least 15915 have completed this online training.
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