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!
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.
Instrumental Variables Methods
This module focuses on causal effect estimation using instrumental variables in both randomized trials with non-compliance and in observational studies. The ideas are illustrated with an instrumental variables analysis in R.
Jerry D. Gardner completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
This was a terrific introduction to causal inference including basic concepts as well as tests and exercises that reinforced learning. One important problem, however, is that the code for some of the exercises had bugs in it. Although I had never used R previously, I was able to identify and correct some of the problems. In the IPTW exercise, I was unable to execute the code and posting my difficulty to the Discussion resulted in a reply that simply reiterated my problem. Hopefully, the instructor will make sure that the code is valid for future attendees.