Causal Inference for Ecologists (CIFE01) SOLD OUT!
Causal Inference for Ecologists is an applied R course teaching researchers how to identify and estimate causal effects in ecological and environmental data.
Duration: 5 Days, 4.5 hours per day
Next Date: March 23-27, 2026
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£400Registration Fee
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5.0
from 200+ reviews
- Overview
- Instructors
- Schedule
Course Description
Ecologists often want to understand causes – that is, how will a response change following some intervention. For example, how will a change in management cause differences in densities of invasive species, or how will increasing temperature affect the growth rate of algae. Recent advances in the field of causal inference outline the methods we need to determine if such questions can be answered by our data – and if so, which models will let us do so.
In this course, we’ll practice using causal inference on both simulated and published datasets. We’ll learn how to write a Directed Acyclic Graph (DAG) for a problem and how to develop sound models. We’ll learn about colliders and confounders and how to avoid them. We’ll look at the perils of using model selection approaches (e.g. AIC) for causal inference. Our examples will cover both experimental and observational datasets.
What You’ll Learn
During the course we will cover the following:
- The building blocks of causal structures between variables
- How to write and analyze a DAG for ecological problems
- The pitfalls of using AIC for causal inference
- How to communicate causal models
- How to apply this framework to your own problems
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is aimed at quantitative scientists working with data to test hypotheses, estimate causal effects, and generate predictions. Participants are expected to have prior experience using R and access to a working installation of linear or mixed-effects modelling software. The course will use a combination of lme4 and rstanarm, covering both frequentist and Bayesian modelling approaches.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
Dr Andrew MacDonald
Andrew is a community ecologist and entomologist, and an applied statistician also. It is the passion for learning, practicing, and talking about statistics that led him into collaborations with scientists from many different fields. Andrew has extensive experience in collaboration and consulting, and now has experience building statistical models of animal behaviour, community composition, occupancy modelling, tree growth, and survey data from humans – to name just a few. He’s especially passionate about helping ecologists connect their theories and their data through expressive statistical models. His favourite tools to do this with are R, brms, and Stan. His goal is to improve our ability to make predictions for ecological systems, and to build a community of practice around statistical methods in ecology.
Education & Career
• PhD in Zoology (Experimental community ecology)
• MSc in Botany (Invasive species ecology)
• Instructor and consultant on a wide range of ecological topics.
Teaching & Skills
• Teaching topics including GLMs, nonlinear models, and hierarchical models – usually with a Bayesian perspective.
• Training and advice regarding reproducible workflows and open science.
• Experienced in collaboration and in developing statistical methods to combine theory with a specific dataset.
Links
Google scholar
Session 1 – 02:00:00 – Principles and Practice of Bayesian Inference for Ecological Data
Break – 00:30:00
Session 2 – 02:00:00 – Principles and Practice of Bayesian Inference for Ecological Data cont.
Session 2 – 02:00:00 – Understanding, Fitting, and Evaluating Random Intercept Models
Break – 00:30:00
Session 3 – 02:00:00 – Understanding, Fitting, and Evaluating Random Intercept Models cont.
Session 5 – 02:00:00 – Motivation, Implementation, and Evaluation of Random Intercept Models
Break – 00:30:00
Session 6 – 02:00:00 – Motivation, Implementation, and Evaluation of Random Intercept Models cont.
Session 7 – 02:00:00 – Random Slopes, Model Structures, and Predictor Preparation in Multilevel Models
Break – 00:30:00
Session 8 – 02:00:00 – Random Slopes, Model Structures, and Predictor Preparation in Multilevel Models cont.
Session 9 – 02:00:00 – GLMMs for Ecological Data – Discrete Outcomes and Model Fit Assessment
Break – 00:30:00
Session 10 – 02:00:00 – GLMMs for Ecological Data – Discrete Outcomes and Model Fit Assessment cont.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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