Causal Inference 2026VT, 6.0 credits Status Cancelled School IDA-gemensam (IDA) Division STIMA Owner José M Peña

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Course plan

No of lectures

5 lectures of 3 hours each.

Recommended for

Students in the field of machine learning, artificial intelligence, or statistics.

The course was last given

In june 2025, as part of the 17th Machine Learning and Advanced Statistics Summer School https://cig.fi.upm.es/mlas/

Goals

Introduction to concepts, tasks, assumptions and limitations of causal inference.

Prerequisites

Basic statistics and probability theory. Basic knowledge of R.

Organization

Lectures, each of which includes some practical exercises in R.

Content

See lectures.

Literature

Hernán M. A. and Robins, J. M. Causal Inference: What If. Chapman &
Hall/CRC, 2020. https://miguelhernan.org/whatifbook

Pearl, J. Causality: Models, Reasoning, and Inference (2nd ed.).
Cambridge University Press, 2009.

Pearl, J., Glymour, M. and Jewell, N. P. Causal Inference in Statistics: A
Primer. Wiley, 2016. https://bayes.cs.ucla.edu/PRIMER/

Lectures

Lecture 0
- Introduction

Lecture 1
- The Fundamental Problem of Causal Inference
- Standardization and Inverse Probability Weighting
- Identifiability and Modeling Assumptions
- Practical Session

Lecture 2
- Structural Causal Models
- Causal Effect Identifiability
- Back-Door and Front-Door Criteria, and do-Calculus
- Counterfactuals
- Practical Session

Lecture 3
- Mediation Analysis
- Direct and Indirect Effects
- Practical Session
- Application
- Causal Inference with Normalizing Flows for Computational Social Science

Lecture 4
- Adjusting for Proxies
- Instrumental Variable Estimation
- Practical Session

Lecture 5
- More on Instrumental Variables
- Control Functions
- Practical Session

Examination

Exercise reports and presentations of results (both in pairs).

Examiner

Jose M. Peña

Credits

6 hp

Comments

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