marginaleffects

Package manual for R and Python, plus a guide to the companion book.

Book: Model to Meaning: How to Interpret Statistical Models in R and Python

•Author: Vincent Arel-Bundock (2026)
•Publisher: CRC Press
•Free online: https://marginaleffects.com
•Print: https://routledge.com/9781032908724

Core framework: Five questions for every analysis

Every interpretation task can be decomposed into five disciplined questions:

•Quantity: What estimand? (predictions, comparisons, slopes, or tests)
•Predictors (Grid): Where to evaluate? (observed values, counterfactual scenarios, balanced grids)
•Aggregation: Over whom? (unit-level, group means with by=, weighted averages)
•Uncertainty: Which inference method? (delta method, robust SE, bootstrap, Bayesian)
•Test: What hypothesis? (null tests, equivalence, pairwise contrasts)

Quick start

Chapter summaries: Read chapters/<chapter>.md Function reference: Read man/r/<function>.md or man/python/<function>.md

When to use this skill

•User asks about predictions, comparisons, slopes, or marginal effects
•User needs help choosing estimands (ATE, ATT, CATE, risk difference, odds ratio)
•User asks about marginaleffects function syntax or arguments
•User wants to interpret model results or test hypotheses
•User mentions counterfactual analysis, G-computation, or causal inference
•User references Model to Meaning chapters

Instructions

•
Classify the request:
- •Conceptual: Which estimand? How to interpret? → Use chapters/
- •Implementation: Function syntax, arguments, code → Use man/r/ or man/python/
- •Mixed: Start with conceptual framing, then provide code
•
Read the relevant source files:
- •Book chapters: chapters/framework.md, chapters/predictions.md, chapters/comparisons.md, chapters/slopes.md, chapters/hypothesis.md, etc.
- •R reference: man/r/predictions.md, man/r/comparisons.md, man/r/slopes.md, man/r/hypotheses.md, man/r/datagrid.md
- •Python reference: man/python/predictions.md, man/python/comparisons.md, man/python/slopes.md, man/python/hypotheses.md
•
Apply the five-question framework to organize your response:
- •Help user define the estimand (Quantity)
- •Clarify where to evaluate it (Grid)
- •Determine aggregation level (Aggregation)
- •Recommend uncertainty quantification (Uncertainty)
- •Specify hypothesis if testing (Test)
•
Provide concrete code examples using the correct function for their language (R or Python)

Available resources

Book chapters (`chapters/`)

File	Topic
`framework.md`	Five-question framework (start here)
`predictions.md`	Predicted values and expected outcomes
`comparisons.md`	Counterfactual comparisons, ATE, ATT, risk ratios
`slopes.md`	Marginal effects, partial derivatives
`hypothesis.md`	Hypothesis testing and equivalence
`interactions.md`	Interaction effects and effect modification
`categorical.md`	Categorical predictors and contrasts
`experiments.md`	Experimental designs
`gcomputation.md`	G-computation and causal inference
`uncertainty.md`	Inference methods (delta, bootstrap, Bayesian)
`weights.md`	Survey weights and weighted estimation
`mrp.md`	Multilevel regression and poststratification
`ml.md`	Machine learning models
`who.md`	Who is this book for?
`challenge.md`	The interpretation challenge

R function reference (`man/r/`)

Core functions (includes avg_* variants): predictions.md, comparisons.md, slopes.md, hypotheses.md Grids: datagrid.md Plots: plot_predictions.md, plot_comparisons.md, plot_slopes.md Utilities: posterior_draws.md, inferences.md, get_dataset.md

Python function reference (`man/python/`)

Core: predictions.md, avg_predictions.md, comparisons.md, avg_comparisons.md, slopes.md, avg_slopes.md, hypotheses.md Grids: datagrid.md Plots: plot_predictions.md, plot_comparisons.md, plot_slopes.md Model fitting: fit_statsmodels.md, fit_sklearn.md, fit_linearmodels.md

Examples

Logit model example

library(marginaleffects)

# Fit logistic regression
mod <- glm(am ~ hp + wt, data = mtcars, family = binomial)

# Average marginal effects (slopes on probability scale)
avg_slopes(mod)

# Predicted probabilities at specific values
predictions(mod, newdata = datagrid(hp = c(100, 150, 200), wt = 3))

# Average treatment effect: compare hp = 150 vs hp = 100
avg_comparisons(mod, variables = list(hp = c(100, 150)))

# Risk ratio for a 50-unit increase in hp
avg_comparisons(mod, variables = list(hp = 50), comparison = "ratio")

Python:

python

import marginaleffects as me
import statsmodels.formula.api as smf

# Fit logistic regression
mod = smf.logit("am ~ hp + wt", data=me.get_dataset("mtcars")).fit()

# Average marginal effects
me.avg_slopes(mod)

# Predicted probabilities at specific values
me.predictions(mod, newdata=me.datagrid(mod, hp=[100, 150, 200], wt=3))

# Average treatment effect: compare hp = 150 vs hp = 100
me.avg_comparisons(mod, variables={"hp": [100, 150]})

User asks about choosing an estimand: → Read chapters/framework.md and chapters/comparisons.md, explain the five-question framework, recommend the appropriate quantity (e.g., avg_comparisons() for ATE).

User asks how to compute marginal effects: → Read man/r/slopes.md or man/python/slopes.md, provide syntax with relevant arguments.

User wants to test treatment effect heterogeneity: → Read chapters/comparisons.md for CATE concepts, then man/r/hypotheses.md for testing syntax with by= groups.

User asks about counterfactual grids: → Read chapters/framework.md (Predictors section) and man/r/datagrid.md for datagrid() usage.

Best practices

•Ask about language preference: If the user hasn't specified R or Python, ask which they prefer before providing code examples
•Always frame responses using the five-question framework when appropriate
•Cite specific sections from summaries or manuals
•Mention get_dataset() when users need example data
•For mixed requests, start with conceptual framing then show implementation