The Statistics of Causal Inference

We start with experiments and move on to look at ways to approach the experimental ideal with observational data. Three pathways to causal inference are covered: 

• instrumental variables
• difference-in-differences
• the regression discontinuity design. 

For each method, we will start with intuition and indicative examples, move on to the identification assumptions, and proceed to estimation.

We will go back to applications, this time thinking more about what type of robustness checks can be implemented to assess the extent to which we can plausibly expect the identification assumptions to hold. For each design we will also cover recent extensions. 

Each technique will be accompanied by a lab session, where you will work with fellow participants to replicate an existing study. Code will be provided in R and in Stata for two examples per method.

How the course will work online 

We will discuss the pre-recorded lectures during the live sessions. Every session will assume you have seen the recording and will be based on questions and discussions on the topics covered. 

Rather than the two-hour lecture that would be happening if this were a face-to-face course, we will provide short videos, each on a specific topic. 

All slides are given one week in advance. The TA will provide support with theory and code. You will be contacted at the end of the second day to discuss how the format works and whether you think changes or ad hoc adjustments would be helpful. For now, the idea is that we will set a slack # for the class group, where we will discuss readings, slides, and code. We may replace this with a Google Colab, but we’ll make this decision after the course is registered. 

There will be ‘refreshment sessions’ on Wednesday, Thursday and Friday, where the Instructor and TA will meet with participants to discuss their own work and research.

You should have a solid understanding of linear regression and even mechanical knowledge of hypothesis testing at the level of any introductory econometrics textbook.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.