WWhile 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 situations preventing randomized experiments. 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 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 to 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.
Each day begins at 13:30 PM (Amsterdaml time) and ends at around 17:00. There will be short breaks each day as needed. However, this schedule will be modified, depending on how well you have learned each topic.
Target Group | The course is aimed at PhD candidates and other academics |
Group Size | Min. 15 / Max. 30 participants |
Course duration | 14 days, afternoons from 13:30 to 17:00 |
Language of instruction | English |
Frequency of recurrence | Once every two or three years |
Number of credits | 1.5 ECTS |
Lecturer | Prof. Bill Shipley (Université de Sherbrooke, Canada) |
Recommended Literature | Cause and correlation in biology : A user’s guide to path analysis, structural equations, and causal inference in R. Cambridge University Press. This book is included in the course fee (as a digital PDF file) and will thus be offered to you about 2 weeks prior to the start of the course. |
Prior knowledge | Prior knowledge Although the emphasis will be on the concepts rather than mathematical properties, some basic knowledge of probability and statistics will be required to understand those concepts. In particular, we will assume familiarity with random variables, joint distributions of random variables, conditional distributions and multiple regression. Basic knowledge of R is recommended (e.g. installing packages, reading data-files, linear regression). |
Location | Online |
EARLY-BIRD FEE 2 | REGULAR FEE 2 | |
PE&RC / WIMEK / WASS / EPS / VLAG / WIAS / RSEE PhD candidates with an approved TSP | € 135,- | € 185,- |
PE&RC postdocs and staff | € 270,- | € 320,- |
All other academic participants | € 310,- | € 360- |
Non academic participants | € 580,- | € 630,- |
1 The course fee includes all course materials (including the PDF versions of the book about Structural Equation Modelling).
2 The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 22 May 2025
Note:
IMPORTANT: ALWAYS read the Cancellation conditions for PE&RC courses and activities.
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