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Advance Your Ecological Data Analysis with Causal Inference

Oliver Hooker 
ausal Inference for Ecologists (CIFE01)

Causal Inference for Ecologists is an applied R course teaching researchers
how to identify and estimate causal effects in ecological and environmental
data.

https://prstats.org/course/causal-inference-for-ecologists-cife01/


*23?27 March 2026 - **Live Online*

Do you work with ecological data and need to move beyond correlation to
understand cause-and-effect? Join our five-day *Causal Inference for
Ecologists* course and gain the practical tools to answer real causal
questions using both experimental and observational datasets.

In this course, you will learn how to:

   -

   Construct and interpret *Directed Acyclic Graphs (DAGs)* to formalise
   causal assumptions.
   -

   Identify and avoid bias from confounders and colliders.
   -

   Understand why traditional model selection methods like AIC can mislead
   causal analysis.
   -

   Apply causal inference frameworks to your own ecological research
   problems.

Our live online format includes lectures, hands-on practical exercises, and
open discussions. All sessions are recorded and made available to
participants across time zones.

*Who Should Attend:* Quantitative scientists with experience in R who are
testing hypotheses, estimating causal effects, or building predictive
models.

*Software:* We will work with *lme4* and *rstanarm*, covering both
frequentist and Bayesian modelling approaches.

*Secure your place today and transform the way you answer causal questions
in ecological research.*
Register at *prstats.org/course/causal-inference-for-ecologists-cife01/
<http://prstats.org/course/causal-inference-for-ecologists-cife01/>*
Email oliver at prstats.org with any questions