Guest Lecture by Vincent Arel-Bundock

Topic: Model to Meaning: How to interpret statistical models

Our world is complex. To make sense of it, data analysts routinely fit sophisticated statistical or machine learning models. Interpreting the results produced by such models can be challenging, and researchers often struggle to communicate their findings to colleagues and stakeholders. This guest lecture is designed to bridge that gap. It offers a practical guide to model interpretation for analysts who wish to communicate their results in a clear and impactful way. It introduces the marginaleffects package (CRAN link, PyPI link) and the conceptual framework that underpins it. The marginaleffects package for R offers a single point of entry for computing and plotting predictions, counterfactual comparisons, slopes, and hypothesis tests for over 100 different types of models. The package provides a simple and unified interface, is well-documented with extensive tutorials, and is model-agnostic—ensuring that users can extract meaningful quantities regardless of the modeling framework they use. The book Model to Meaning: How to Interpret Statistical Results Using marginaleffects for R (forthcoming with CRC Chapman & Hall) introduces a powerful conceptual framework to help analysts make sense of complex models. It demonstrates how to extract meaningful quantities from model outputs and communicate findings effectively using marginaleffects. The guest lecture will provide a deep understanding of how to use marginaleffects to improve model interpretation, and show how to compute and visualize key statistical summaries, including marginal means, contrasts, and slopes, and how to leverage marginaleffects for hypothesis and equivalence testing. The package follows tidy principles, ensuring that results integrate seamlessly with workflows in R, and with other packages such as ggplot2, quarto, and modelsummary. The guest lecture is suitable for researchers, students, data scientists, and analysts who fit statistical models in R and seek an easy, reliable, and transparent approach to model interpretation. No advanced mathematical background is required, but familiarity with generalized linear models like logistic regression is assumed.

Bio: Vincent Arel-Bundock is a Professor in the Department of Political Science at the Université de Montréal. His research focuses on comparative and international political economy, with particular interests in research methodology and the politics of international taxation. He is an active contributor to several open-source software projects and an Executive Editor of The R Journal.

Schedule: The lecture will take place over Zoom on Wednesday, April 15, at 4pm Central.