Causality and machine learning with applications in psychology

12 et 19 mai 2026, 9h-18h

Dr Matthew Vowels, Kivira Health, The Sense (CHUV, Lausanne)

What is the current problem?
In spite of a general recognition that causality is a fundamental part of the scientific endeavour to understand the world, causal approaches to analysis are still taboo in much of psychology and social science. Furthermore, the validity of most of the popular techniques being used rests on assumptions which we already know not to hold in practice (linearity, parametric form, etc.). The result of these two shortcomings is poor replicability, meaningless interpretations of results, and theories which cannot be formalized or tested.

Solution – This course offers PhD students conceptual and methodological state-of-the-art approaches from the intersection of causal inference, causal discovery, and introductory machine learning to overcome the current limitations. We focus on Directed Acyclic Graphs (DAGs) for causality in general, constraint-based approaches for causal discovery, and an accessible overview of ensemble methods and related predictive techniques, as well as combinations of these for testing our theories and hypotheses.

Benefits - This will help students to (a) develop, enrich, and formalize their theories so that they can test them unambiguously, (b) identify key control variables in order to render interpretable and causal results, and (c) use modern approaches to predictive machine learning. Together, this brings psychology in line with modern approaches to analysis without sacrificing interpretability. (Overview of this topic: https://doi.org/10.1177/1745691620921521)

UNIL, bâtiment Amphipôle, salle 321

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