Parameter estimates are often hard to interpret substantively, especially when they are generated by complex models with non-linear components, interactions, or transformations. Instead of reporting unintuitive parameters, data analysts would rather focus on simple quantities of interest, which have straightforward scientific interpretations. Unfortunately, meaningful estimands—and their standard errors—are often tedious to compute, and the terminology used to describe them varies tremendously across fields.

These problems are compounded by the fact that modeling packages produce objects with very different structures and which hold different information. This means that end-users often have to write customized code to interpret the output of different models. This can lead to wasted effort, confusion, and mistakes, and it can hinder the implementation of best practices.

Marginal Effects Zoo: The Book

This free online book introduces a conceptual framework to clearly define statistical quantities of interest, and shows how to estimate those quantities using the marginaleffects package for R and Python. The techniques introduced herein can enhance the interpretability of over 100 classes of statistical and machine learning models, including linear, GLM, GAM, mixed-effects, bayesian, categorical outcomes, XGBoost, and more. With a single unified interface, users can compute and plot many estimands, including:

• Predictions (aka fitted values or adjusted predictions)
• Comparisons such as contrasts, risk differences, risk ratios, odds, etc.
• Slopes (aka marginal effects or partial derivatives)
• Marginal means
• Linear and non-linear hypothesis tests
• Equivalence tests
• Uncertainty estimates using the delta method, bootstrapping, simulation, or conformal inference.
• Much more!

The Marginal Effects Zoo book includes over 30 chapters of tutorials, case studies, and technical notes. It covers a wide range of topics, including how the marginaleffects package can facilitate the analysis of:

• Experiments
• Observational data
• Causal inference with G-Computation
• Machine learning models
• Bayesian modeling
• Multilevel regression with post-stratification (MRP)
• Missing data
• Matching
• Inverse probability weighting
• Conformal prediction

Get started by clicking here!

marginaleffects: The Package

The marginaleffects package for R and Python offers a single point of entry to easily interpret the results of over 100 classes of models, using a simple and consistent user interface. Its benefits include:

• Powerful: It can compute and plot predictions; comparisons (contrasts, risk ratios, etc.); slopes; and conduct hypothesis and equivalence tests for over 100 different classes of models in R.
• Simple: All functions share a simple and unified interface.
• Documented: Each function is thoroughly documented with abundant examples. The Marginal Effects Zoo website includes 20,000+ words of vignettes and case studies.
• Efficient: Some operations can be up to 1000 times faster and use 30 times less memory than with the margins package.
• Valid: When possible, numerical results are checked against alternative software like Stata or other R packages.
• Thin: The R package requires relatively few dependencies.
• Standards-compliant: marginaleffects follows “tidy” principles and returns objects that work with standard functions like summary(), tidy(), and glance(). These objects are easy to program with and feed to other packages like ggplot2 or modelsummary.
• Extensible: Adding support for new models is very easy, often requiring less than 10 lines of new code. Please submit feature requests on Github.
• Active development: Bugs are fixed promptly.

How to help

The marginaleffects package and the Marginal Effects Zoo book will always be free. If you like this project, you can contribute in four ways:

1. Make a donation to the Native Women’s Shelter of Montreal or to Give Directly, and send me (Vincent) a quick note. You’ll make my day.
2. Submit bug reports, documentation improvements, or code contributions to the Github repositories of the R version or the Python version of the package.
3. Cite the marginaleffects package in your work and tell your friends about it.
4. Create a new entry for the Meme Gallery!