MATH-336 Randomization and causation

Professor: Mats J. Stensrud
TA: Elise Dumas


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