Model To Meaning: How To Interpret Statistical Models with MarginalEffects
Vincent Arel-Bundock
July 29, 2026 – 9am-1pm EST, 1430 ISR-Thompson/Zoom
Please join for the next installment of the PDHP workshop series: Model To Meaning: How To Interpret Statistical Models with Marginaleffects, presented by Vincent Arel-Bundock of the University of Montreal. Following the presenter’s book on the same topic, this workshop introduces the model to meaning conceptual framework, helping data analysts of all types to clearly and rigorously communicate model results, from (almost) any statistical model. Relying on the key idea that raw parameter estimates can often be transformed into more interpretable quantities, the model to meaning framework provides a powerful toolset for analysts of all experience levels.
Topics include:
- Model to Meaning as a simple framework to clearly define and communicate your quantities of interest
- Interpreting the results of (almost) any statistical model with a single workflow and toolset
- Comparing levels and effects across groups and scenarios to assess heterogeneity
- Hands-on practice with the marginaleffects package in R and Python
Zoom Link:
TBA
Video, Slides, and Lab Materials:
All workshop materials (including video & slides) will be posted here after the conclusion of the workshop
Recommended Software:
The workshop will provide examples in R as configured below:
–– R Studio (strongly recommended)
–– Marginaleffects Python package
–– R packages can be installed using the following code
# install R packages
install.packages(c("marginaleffects"))