Randomization and causation

Professor: Mats J. Stensrud
TAs: Matias Janvin and Laya Ghodrati

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.