StatsPAI

Install

pip install statspai

Then:

import statspai as sp

print(sp.datasets.list_datasets()[["name", "design", "n_obs"]].head())

StatsPAI ships teaching datasets such as Card (1995), Callaway-Sant'Anna mpdta, Lee (2008) RD, LaLonde/NSW, and California Proposition 99. The examples below run offline after installation.

At a glance: 1,182 registered functions across 87 submodules; 352k LOC (core) + 200k LOC (tests). Run python scripts/registry_stats.py to reproduce these numbers.

---

Beginner Examples With Results

The outputs below are rounded from the bundled examples in this repository using StatsPAI 1.20.0.

{'status': 'bit-exact', 'reference': 'fixest::feols',

'reference_versions': {'R': '...4.5.2...', 'fixest': '0.14.0'},

Everyday Workflow

import statspai as sp

card = sp.datasets.card_1995()
r1 = sp.regress(
    "lwage ~ educ + exper + expersq + black + south + smsa",
    data=card,
    robust="hc1",
)
r2 = sp.ivreg("lwage ~ (educ ~ nearc4) + exper + expersq + black + south + smsa", data=card)

print(r1.summary())                         # human-readable table
print(r1.tidy().head())                      # broom-style dataframe
sp.modelsummary(r1, r2, output="table.docx") # Word table
sp.outreg2(r1, r2, filename="results.xlsx")  # Stata-style export

Useful docs:

• Getting started
• Cookbook
• Choosing an IV estimator
• Choosing a DID estimator
• Choosing an RD estimator
• Migrating from R to StatsPAI
• Exporting regression tables

---

Compared With Other Python Causal Packages

StatsPAI is meant to be the broad Stata/R-style workbench for applied empirical research, not only a single modeling family.

Use causallib when you mainly want sklearn-style treatment-effect pipelines. Use CausalPy when you want Bayesian causal modeling in PyMC. Use StatsPAI when you want one Python package to replace the everyday Stata/R empirical workflow.

---