Causal Inference with Graphical Models2020HT
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 with the help of graphical
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 Wright (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 Pearl (2009) and Peters et al. (2017). The goal of this course is to introduce the students to these works.
Students in the field of machine learning, artificial intelligence, or statistics.
Basic statistics and probability theory.
Lecture 0: Introduction
Lecture 1: Causal Models and Learning Algorithms
Lecture 2: Causal Effect Identification and do-Calculus
Lecture 3: Actions, Plans and Direct Effects
Lecture 4: Linear-Gaussian Causal Models
Lecture 5: Counterfactuals
Pearl, J. Causality: Models, Reasoning, and Inference. Cambridge University
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, seminars and lab.
Lab report and seminar presentations (both in pairs).
Jose M. Peña
Jose M. Peña
Page responsible: Director of Graduate Studies
Last updated: 2012-05-03