Data Science & Analytics
Marginaleffects
The Marginaleffects skill provides expert guidance on the R and Python package for computing marginal effects, predictions, and average treatment effects, as well as the accompanying book “Model to Meaning.” It assists analysts and researchers in generating predictions, comparisons, slopes, and hypothesis tests, and in visualizing results with plot functions. The skill is ideal for data scientists and statisticians who need precise marginal effect calculations, counterfactual analysis, and causal inference using G‑computation.
- Publisher / source
- brycewang-stanford/Auto-Empirical-Research-Skills (found on GitHub top repositories)
- Upstream record
- brycewang-stanford/Auto-Empirical-Research-Skills
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
a1fe86ef4f9bb6cea81cfb586335903b19616fe02572671e16cb6f57a263b90e