Scope: While much of statistics focuses on associations between variables and making predictions, the aim of structural equation modelling is to establish causal relationships between variables. In spite of the common belief that any causal statement requires randomized experiments, there is an increasing body of theory, methodology and software which enables scientists to draw certain types of causal conclusions from observational data. This has important advantages, especially in cases where randomized experiments are not feasible.
Notably, causal models allow the quantification of intervention effects, which is the response of the system given a certain value of one your variables (e.g. gene knock-out, rainfall). This course will explain the key concepts underlying causal inference, the required assumptions, and how the interpretation of results differs from the case of randomized experiments.
Target group: The course is aimed at PhD candidates, postdocs, and other academics
Course duration: 5 days
Contact: PE&RC Office: office.pe@wur.nl
Registration of interest: You can register your interest HERE (note: this is not an official registration).