Causal Inference for Ecologists (CIFE01) *Applied R Training for Marine Mammal Researchers* *23–27 March 2026 | Live Online* Marine mammal researchers are often tasked with answering causal questions from complex ecological data: *How does shipping noise affect whale behaviour? Do management interventions reduce bycatch risk? How will changing ocean temperatures influence survival or distribution?* Addressing these questions requires methods that go beyond correlation. *Causal Inference for Ecologists* is a five-day, applied R course designed to identify and estimate causal effects using both experimental and observational data. The course provides a practical framework for determining when causal questions can be answered with available data—and how to model them appropriately. In this course, you will learn how to: - Construct and interpret *Directed Acyclic Graphs (DAGs)* to formalise causal assumptions. - Identify and avoid bias arising from *confounders, colliders, and inappropriate conditioning* - Understand why common model selection approaches (e.g. AIC) can be misleading for causal inference - Apply causal inference principles to real-world problems The live online format combines short lectures with hands-on coding exercises and discussion. All sessions are recorded and available to participants across time zones. Who Should Attend This course is aimed at quantitative scientists with experience in R who are testing hypotheses, estimating causal effects, or developing predictive models from ecological data. Software We will work in R using *lme4* and *rstanarm*, covering both frequentist and Bayesian modelling approaches commonly used in marine mammal research. *Secure your place and strengthen the causal foundations of your research.* Register at: https://prstats.org/course/causal-inference-for-ecologists-cife01/ <https://prstats.org/course/causal-inference-for-ecologists-cife01/?utm_source=chatgpt.com> For enquiries, email *oliver@prstats.org* -- Oliver Hooker PhD. PR stats