OpenSkillIndex← back to search
claude-code

Awesome-Agent-Skills-for-Empirical-Research marginaleffects

Manual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal inference with G-computation, or need help with marginaleffects functions like predictions(), comparisons(), slopes(), hypotheses(), datagrid(), avg_predictions(), avg_comparisons(), avg_slopes(), or plot functions.

install
source · Clone the upstream repo
git clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/39-vincentarelbundock-marginaleffects" ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-marginaleffects && rm -rf "$T"
manifest: skills/39-vincentarelbundock-marginaleffects/SKILL.md
source content

marginaleffects

Primary source of information: https://marginaleffects.com Free book, case studies, and vignettes are available there.

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

Core framework: Five questions for every analysis

Every interpretation task can be decomposed into five disciplined questions:

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

Quick start

Chapter summaries: Read 

chapters/<chapter>.qmd
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

  1. 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

  2. Read the relevant source files:

    • Book chapters: 
      chapters/framework.qmd
      , 
      chapters/predictions.qmd
      , 
      chapters/comparisons.qmd
      , 
      chapters/slopes.qmd
      , 
      chapters/hypothesis.qmd
      , 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

  3. 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)

  4. Provide concrete code examples using the correct function for their language (R or Python)

Available resources

Book chapters (
chapters/
)

FileTopicChapter focus
framework.qmd
Five-question framework (start here)Defines the five questions and core quantities (predictions, comparisons, slopes) for turning models into intuitive estimands.
predictions.qmd
Predicted values and expected outcomesDefines predictions, grids, aggregation, and tests with 
predictions()
/
avg_predictions()
.
comparisons.qmd
Counterfactual comparisons, ATE, ATT, risk ratiosDefines counterfactual comparisons, effect functions, grids, and aggregation with 
comparisons()
/
avg_comparisons()
.
slopes.qmd
Marginal effects, partial derivativesDefines slopes as partial derivatives, conditional on predictors; uses 
slopes()
/
avg_slopes()
.
hypothesis.qmd
Hypothesis testing and equivalenceNull vs equivalence tests for any quantity using 
hypothesis
and 
equivalence
arguments.
interactions.qmd
Interaction effects and effect modificationInterprets heterogeneity and nonlinearity with interactions and polynomials using predictions, comparisons, and slopes.
categorical.qmd
Categorical predictors and contrastsApplies the framework to categorical/ordinal outcomes with predictions and comparisons by outcome level.
experiments.qmd
Experimental designsATE in experiments and factorial designs via 
avg_comparisons()
and robust SEs.
gcomputation.qmd
G-computation and causal inferenceG-computation steps for ATE/ATT/ATU/CATE with counterfactual prediction grids.
uncertainty.qmd
Inference methods (delta, bootstrap, Bayesian)Delta method, bootstrap, simulation, conformal prediction, and robust/clustered standard errors via 
inferences()
/
vcov
.
mrp.qmd
Multilevel regression and poststratificationMultilevel models and poststratification with predictions and comparisons in mixed effects.
ml.qmd
Machine learning modelsModel auditing with predictions, comparisons, and slopes for ML frameworks.
challenge.qmd
The interpretation challengeDefines analysis goals, estimands, and why coefficients need transformation.

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

R:

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:

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.qmd
and 
chapters/comparisons.qmd
, 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.qmd
for CATE concepts, then 
man/r/hypotheses.md
for testing syntax with 
by=
groups.

User asks about counterfactual grids: → Read 

chapters/framework.qmd
(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