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Causal Inference with Graphical Models2024HT
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Course plan
No of lectures
6
Recommended for
Students in the fields of machine learning, artificial intelligence, statistics, and computational social sciences.
The course was last given
HT2020
Goals
Causal inference comprises the study of cause and effect relationships, e.g.
the conditions under which they can be elucidated from observations and/or be
used to compute causal effects without actually performing interventions. More
specifically, when can we determine from observations if our habits are the
cause of a certain disease ? Or, when can we compute from observations the
effect on our health of a prescribed treatment ? The goal of this course is to
show the students how to answer these questions from observational data with
the help of graphical models.
Since predicting the consequences of decisions or actions is necessary in many
disciplines, it is not surprising that research on causal inference has a long
tradition. Specifically, causal inference can be traced back to the work by
Sewall Wright in 1921, where path analysis was introduced for the first time.
In path analysis causal relationships are represented with directed edges, and
correlations due to unobserved common causes are represented with bidirected
edges. Wright showed how to use such a graph-based model (a.k.a graphical
model) to perform causal inference. Since then, the field has grown and
matured: New graphical models have been proposed, new algorithms for causal
effect identification have been developed, and algorithms for learning causal
relationships from observations have been devised. Most of these results are
reported in the books by Judea Pearl. The goal of this course is to introduce
the students to these results.
Prerequisites
Basic statistics and probability theory.
Organization
The course consists of six lectures. They follow the book by Pearl et al. (2016). Therefore, access to the book is recommended (e.g., LiU library has an online copy). The lectures are mainly theoretical. They are then complemented with a lab to put in practice the theory learned. The students are expected to hand in a report with their lab results. This is the first part of the examination. The second part of the examination consists of a seminar presentation of a scientific article, followed by discussion.
Content
Causal inference, graphical models, interventions, counterfactuals, and mediation analysis.
Literature
Hernán M. A., Robins J. M. Causal Inference: What If. Chapman & Hall/CRC, 2020.
Pearl, J. Causality: Models, Reasoning, and Inference. Cambridge University
Press, 2009.
Pearl, J., Glymour, M. and Jewell, N. P. Causal Inference in Statistics. A
Primer. J. Wiley & Sons, 2016.
Peters, J. and Janzing, D. and Schölkopf, B. Elements of Causal Inference:
Foundations and Learning Algorithms. MIT Press, 2017.
Lectures
Lecture 1: Introduction
Lecture 2: Causal Models and Learning Algorithms
Lecture 3: Causal Effect Identification and do-Calculus
Lecture 4: Direct and Indirect Effects
Lecture 5: Linear-Gaussian Causal Models
Lecture 6: Counterfactuals
Examination
Lab report and seminar presentation of scientific paper (both in pairs).
Examiner
Jose M. Peña
Credits
6 HEC
Comments
Page responsible: Director of Graduate Studies
