Causal thinking

Summary

This course will give a unified presentation of modern methods for causal inference. We focus on concepts, and we will present examples and ideas from various scientific disciplines, including medicine, computer science, engineering, economics and epidemiology.

Content

Keywords

Causality; Causal inference; Randomisation; Design of experiments; Observational studies; Causal Graphs

Learning Prerequisites

Required courses

The course is intended for students from a range of different disciplines, including computer science, engineering, life science and physics. The students are expected to know the basics of statistical theory and probability theory (such as the second year courses in probability and statistics for engineers).

Recommended courses

Courses in statistical inference.

Important concepts to start the course

Familiarity with basic concepts in probability and statistics.

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

Teaching methods

Classroom lectures, where I will use a digital blackboard and slides.

Assessment methods

Final written exam. 1-2 graded homeworks.

Supervision

Resources

Bibliography

Hernan, M.A. and Robins, J.M., 2020. Causal inference: What if?

Imbens, G.W. and Rubin, D.B., 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press.

Pearl, J., 2009. Causality. Cambridge university press.

Ressources en bibliothèque

• Causal inference in statistics, social, and biomedical sciences / Imbens
• Causality / Pearl
• Causal Inference / Hernan

Moodle Link

• https://go.epfl.ch/MATH-352