A Crash Course in Causality: Inferring Causal Effects from Observational Data

You have heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to provide an answer to that question, as well as others! You will learn how to define causal effects, what assumptions about your data and models are required, and how to implement and interpret some popular statistical methods over the course of 5 weeks. Learners will be able to apply these methods to real-world data in R. (free statistical software environment).

Learners should be able to define causal effects using potential outcomes, explain the difference between association and causation, and express assumptions using causal graphs by the end of the course. You'll also learn how to use a variety of causal inference methods (for example, matching, instrumental variables, and inverse probability of treatment weighting) and determine which causal assumptions are required for each statistical method.

This course offers:

• Flexible deadlines: Reset deadlines based on your availability.
• Shareable certificate: Get a Certificate when you complete
• 100% online
• Intermediate level
• Approx. 18 hours to complete
• Subtitles: French, Portuguese (European), Russian, English, Spanish

Course ratings: 4.7/5

Enroll here: https://www.coursera.org/learn/crash-course-in-causality