# Fiches de cours

## Randomization and causation

#### Enseignant(s) :

Stensrud Mats Julius

English

#### Summary

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

#### Keywords

Causality; Causal inference; Randomisation; Experimental design: Structural equation models; Causal Graphs; Estimands.

#### Learning Prerequisites

##### Required courses

The students are expected to know the basics of statistical theory and probability theory. The courses 'probability' (Math-230), 'statistics' (Math-240) and 'linear models' (Math-341).

##### Recommended courses

Courses in regression models and statistical inference.

##### Important concepts to start the course

Likelihood theory and principles of statistical testing. Experience with R is an advantage, but is not required.

#### 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
• Critique 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.

#### Transversal skills

• Demonstrate the capacity for critical thinking
• Communicate effectively, being understood, including across different languages and cultures.

#### Teaching methods

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

#### Assessment methods

Final written exam and a mini project.

#### Resources

##### Bibliography

Teaching resources

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

### Semaine de référence

LuMaMeJeVe
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
En construction

Cours
Exercice, TP
Projet, autre

### légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand