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Causal Inference with Graphical Models
  • Goal
    Causal inference comprises the study of cause and effect relationships, e.g. the conditions under which they can be elucidated from observations and, then, 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 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 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.

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

  • Prerequisites
    Basic statistics and probability theory.

  • Organization
    Lectures, seminars and lab.

  • Examination
    Lab report and seminar presentations (both in pairs).

  • Credits
    6 HEC

  • Contents
    Lecture 0 (10/11/2020 15-17 via Zoom): Introduction (slides)
    Lecture 1 (11/11/2020 15-17 via Zoom): Causal Models and Learning Algorithms (slides, code)
    Lecture 2 (16/11/2020 15-17 via Zoom): Causal Effect Identification and do-Calculus (slides, code)
    Lecture 3 (17/11/2020 15-17 via Zoom): Actions, Plans and Direct Effects (slides)
    Lecture 4 (18/11/2020 15-17 via Zoom): Linear-Gaussian Causal Models (slides)
    Lecture 5 (23/11/2020 15-17 via Zoom): Counterfactuals (slides)
    Lecture 6 (24/11/2020 15-17 via Zoom): Non-graphical Causal Inference (slides)
    Lab: Deadline 04/12/2020 (exercises)
    Seminar presentations (25-26/11/2020 15-17 via Zoom): Choose and present one of the following articles, or one of own your choice after approval by the examiner.
  • Literature
    Hernán M. A. and Robins J. M. Causal Inference: What If. Chapman & Hall/CRC, 2020.
    Pearl, J. Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press, 2009.
    Pearl, J. Causality: Models, Reasoning, and Inference (1st ed.). Cambridge University Press, 2000.
    Pearl, J., Glymour, M. and Jewell, N. P. Causal Inference in Statistics. A Primer. J. Wiley & Sons, 2016.
    Peters, J., Janzing, D. and Schölkopf, B. Elements of Causal Inference. MIT Press, 2017.

  • Lecturer and examiner
    Jose M. Peña

Page responsible: Jose M. Peña
Last updated: 2020-11-19