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While much of statistics focusses on associations between variables and making predictions, the aim of structural equation modeling 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 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. The focus will be on classical structural equation models with a small number of (latent) variables, but we will also give an introduction to recent developments on methodology for high-dimensional data. Throughout the course we will discuss applications in ecology, social sciences and genetics. 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.
|Prior knowledge||Basic knowledge of probability and statistics|
|Intended credits||1.5 ECTS|
|Course organisation||The C.T. de Wit Graduate School for Production Ecology and Resource Conservation (PE&RC)|
|More information||Course website|