Course info

Professor: Mats J. Stensrud
TA: Lorenzo Gasparollo

Motivation

This course covers formal frameworks for causal inference. We focus on experimental designs, definitions of causal models, interpretation of causal parameters and estimation of causal effects.

Content

• Experimental design
• Randomisation
• Matched pairs, block designs, (fractional) factorial designs and latin squares
• 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

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

Zoom lectures, where I will use Beamer slides and the digital blackboard.

Feel free to discuss on Piazza (password CAUSATION)

Assessment methods

Final written exam (90%) and one graded homework (10%)

The graded homework will be given to you 25th April and you need to submit 2nd May 23h00. 

Teaching resources

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