About this Course
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!
What You'll Learn
- Define causal effects using potential outcomes
- Distinguish association from causation
- Implement and interpret causal inference methods
- Apply methods to data using R software
Prerequisites
- Basic familiarity with statistics and quantitative research concepts
- Readiness to practice through data-based exercises
Instructors
Jason A. Roy, Ph.D.
Professor of Biostatistics