Online course: Bayesian Causal Networks in R for researchers working with SEM and causal models

[Carlo Pecoraro]

Dear all,

If you are interested in moving beyond traditional association models to better understand and represent causal relationships among variables, we would like to invite you to our online course Bayesian Causal Networks in R, taking place 7–11 September.

Course website: https://www.physalia-courses.org/courses-workshops/bayesian-netwok/

Structural equation models and Bayesian causal networks share a common goal: representing complex relationships among variables and evaluating hypotheses about how systems operate. Bayesian causal networks provide an additional framework for incorporating uncertainty, combining prior knowledge with data, and performing probabilistic inference.

This hands-on course introduces the theory and practical implementation of Bayesian causal networks using R. Participants will learn how to define causal assumptions using directed acyclic graphs (DAGs), perform structure and parameter learning, conduct inference and scenario analysis, and validate their models using real and simulated datasets.

Through five interactive online sessions, participants will gain practical experience with causal modelling, model construction, uncertainty quantification, and interpretation of complex multivariate systems.

The course is aimed at researchers, PhD students, and professionals interested in causal inference, probabilistic modelling, and advanced statistical approaches using R.

For the full programme, prerequisites, and registration, please visit our course webpage.

Best regards,

Carlo

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Carlo Pecoraro, Ph.D.
Physalia Courses Director
in...@physalia-courses.org