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The Statistics of Causal Inference

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Monday 10 ꟷ Friday 14 August 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements

Elias Dinas

European University Institute

This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.

Purpose of the course

This course introduces you to the principles, logic and perquisites of causal inference. Using the potential outcomes framework, together with elements from Pearl's Directed-Acyclic-Graph treatment of causality, it aims to familiarise you with design-based approaches to causal inference. We will try to set best practices about how to think and discuss causal effects.

ECTS Credits

3 credits Engage fully with class activities
4 credits Complete a post-class assignment

Instructor Bio


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.