While much of statistics focusses on associations between variables and making predictions, the aim of structural equation modelling is to test multivariate causal hypotheses and to estimate 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 that 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. rainfall). This new 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. To ensure that you learn from the best, we managed to get Prof. Bill Shipley from the Université de Sherbrooke in Canada to come over to Wageningen to actually give this course. Prof. Shipley is the author of “Cause and correlation in biology: A user’s guide to path analysis, structural equations, and causal inference with R”, which by many is seen as the guide for working with Path Analysis and Structural Equation Models. The focus will be on classical structural equation models with latent variables and generalisations of path analysis via d-separation and directed acyclic graphs using the R program. Throughout the course we will discuss applications in ecology, evolution, and other areas of biology. Depending on the background and interests of the participants we may put a stronger emphasis on some of these applications. Participants are therefore encouraged to bring their own data.
Former occurrences of this course
12-16 Oct 2020 | 22-26 Jan 2018 | 14-18 Dec 2015