marginaleffects 0.0.7

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The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes non-linear components, interactions, or transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to understand, such as predictions, contrasts, risk differences, ratios, odds, lift, slopes, and so on.

Unfortunately, computing these quantities---along with associated standard errors---can be a tedious and error-prone task. This problem is compounded by the fact that modeling packages in R and Python produce objects with varied structures, which hold different information. This means that end-users often have to write customized code to interpret the estimates obtained by fitting Linear, GLM, GAM, Bayesian, Mixed Effects, and other model types. 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!

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!