Path Analysis, Structural Equations and Causal Inference for Biologists (PSCB04) – Applications for Evolutionary Biology https://prstats.org/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb04/ Delivered by Prof. John William (Bill) Shipley, Professor Emeritus at Université de Sherbrooke and a plant and statistical ecologist internationally recognised for his work on causal modelling, path analysis and structural equation modelling. Prof. Shipley is the author of Cause and Correlation in Biology and has published more than 170 peer-reviewed papers. Learn how to use path analysis, structural equation modelling (SEM) and causal inference in R to investigate complex cause-and-effect relationships in evolutionary and biological data. Evolutionary systems frequently involve networks of interacting processes rather than simple relationships between individual predictors and responses. Environmental conditions, phenotypic traits, evolutionary history and other biological processes can exert both direct and indirect effects. This course provides a rigorous framework for translating such relationships into explicit causal hypotheses and evaluating them using observational data typical of ecological and evolutionary research. What you'll gain Understand causal inference using experimental and observational data Translate biological and evolutionary hypotheses into directed acyclic graphs (DAGs) Understand d-separation and statistical conditioning Construct and test causal path models Distinguish direct and indirect causal effects Conduct piecewise structural equation modelling Use the piecewiseSEM package in R Incorporate mixed models and phylogenetic corrections Understand equivalent models and compare competing causal hypotheses Use AIC to compare non-equivalent models Conduct covariance-based path analysis Fit structural equation models using lavaan Construct and interpret latent variables and measurement models Incorporate nested and mixed models into SEM Work with non-normal biological data Fit and interpret multigroup models Understand causal heterogeneity Evaluate model fit and statistical power Deal with missing data and small sample sizes Report SEM results appropriately in scientific publications Course format Dates: 19–30 October 2026 Duration: 35 hours Approximately 20 hours of lectures and 15 hours of practical work Format: Live online Practical analyses conducted in R and RStudio R scripts, datasets and supporting materials provided Opportunities to work with and receive feedback on your own data All sessions recorded 30 days' access to recordings 30 days of post-course support Course fee: £450 Who is this course for? The course is particularly suitable for students and researchers working in: Evolutionary biology Evolutionary ecology Comparative biology Trait evolution and functional biology Phenotypic variation Behavioural evolution Life-history research Evolutionary responses to environmental variation Quantitative and statistical evolutionary biology It will be particularly useful for researchers working with observational datasets containing multiple interacting variables, where the aim is to move beyond identifying correlations and instead formulate and evaluate hypotheses about the causal processes connecting biological variables. Participants should have experience using R and RStudio for statistical analysis and a basic understanding of statistical inference and regression. Familiarity with GLMs or mixed models is useful but not required. Why take this course? Evolutionary questions often involve interconnected causal pathways. Environmental variation may influence a phenotype directly while also affecting other traits or biological processes that subsequently influence the same response. Similarly, apparent associations between traits can arise through indirect pathways, confounding variables or shared biological structure. Path analysis and SEM provide a framework for representing these hypotheses explicitly and evaluating whether observed data are consistent with the proposed causal structure. Participants will learn both piecewise SEM, which can accommodate mixed models and phylogenetic corrections, and covariance-based SEM, including latent-variable approaches. Importantly, the course goes beyond learning how to fit SEMs in R. It develops the underlying logic of causal inference from observational biological data, including d-separation, equivalent models, direct and indirect effects, and the circumstances under which causal interpretations can be justified. These methods provide evolutionary biologists with a powerful framework for testing complex hypotheses involving multiple interacting processes. Learn more & enrol Path Analysis, Structural Equations, and Causal Inference for Biologists (PSCB04) https://prstats.org/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb04/ Questions? Email: oliver@prstats.org Best wishes, Oliver Hooker PR Stats (to subscribe/unsubscribe the EvolDir send mail to evoldir@evoldir.net)