Randomization and causation

Lecturer: Nils Sturma
TA: Julien Laurendeau

Motivation

This course covers formal frameworks for causal inference. We focus on definitions of causal models, interpretation of causal parameters, estimation of causal effects and experimental designs. We put emphasis on how these methods can be used to answer practically relevant questions.

Content

• Defining a causal model
• Causal axioms
• Falsifiability
• Structural equations
• Causal directed acyclic graphs
• Single world intervention graphs
• Interpretation of causal parameters 
• Individual and average level effects
• Mediation and path specific effects
• Instrumental variables
• Statistical inference: Estimands, estimators and estimates
• Relation to classical statistical models
• Doubly and multiply robust estimators
• Experimental design
• Randomisation
• Matched pairs, block designs, (fractional) factorial designs and latin squares

Learning Outcomes

By the end of the course, the student must be able to:

• Design experiments that can answer causal questions. 
• Describe the fundamental theory of causal models. 
• Critically assess causal assumptions and axioms. 
• Distinguish between interpretation, identification and estimation. 
• Describe when and how causal effects can be identified and estimated from non-experimental data.
• Estimate causal parameters from observational data.

Teaching methods

Lectures, where I will use the blackboard. The sessions will not be recorded. 

The TA will respond to questions on Ed Discussion (see the link below). Please use Ed Discussion for all questions about the course. 

Assessment methods

Final exam (80% of the total grade) and one midterm exam (20% of the total grade). The midterm will be on April 13, 2025, during the lecture.

Lecture notes

The course is based on material of Mats J. Stensrud, who teached the course in previous years. I will make his slides available each week.

Teaching resources

• Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?
• Pearl, J., 2009. Causality. Cambridge university press.