StatsPAI 0.9.2

The Agent-Native Causal Inference & Econometrics Toolkit for Python

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StatsPAI: The Agent-Native Causal Inference & Econometrics Toolkit for Python

StatsPAI is the agent-native Python package for causal inference and applied econometrics. One import, 390+ functions, covering the complete empirical research workflow — from classical econometrics to cutting-edge ML/AI causal methods to publication-ready tables in Word, Excel, and LaTeX.

Designed for AI agents: every function returns structured result objects with self-describing schemas (list_functions(), describe_function(), function_schema()), making StatsPAI the first econometrics toolkit purpose-built for LLM-driven research workflows — while remaining fully ergonomic for human researchers.

It brings R's Causal Inference Task View (fixest, did, rdrobust, gsynth, DoubleML, MatchIt, CausalImpact, ...) and Stata's core econometrics commands into a single, consistent Python API.

🎉 NEW in v0.9.0 — Synthetic Control: Most Comprehensive SCM Toolkit in Any Language

20 SCM estimators + 6 inference strategies + full research workflow, all behind a unified sp.synth(method=...) dispatcher. No competing package in Python, R, or Stata offers this breadth.

What	Coverage
20 estimators	classic, penalized, demeaned, unconstrained, augmented (ASCM), sdid, gsynth, staggered, mc (matrix completion), discos (distributional), multi_outcome, scpi (prediction intervals), bayesian (MCMC), bsts / causal_impact, penscm (Abadie-L'Hour), fdid (Forward DID), cluster, sparse (LASSO), kernel, kernel_ridge
6 inference	placebo · conformal · bootstrap · jackknife · Bayesian posterior · BSTS posterior
Research workflow	synth_compare() runs all 20 · synth_recommend() auto-selects · synth_power() + synth_mde() · synth_sensitivity() (LOO + time placebos + donor + RMSPE) · synth_report(format='latex') one-click publication
Canonical datasets	california_tobacco(), german_reunification(), basque_terrorism()
Correctness	ASCM re-implemented to Ben-Michael et al. (2021) Eq. 3; Bayesian MCMC Jacobian corrected; 9 release-blocker fixes from a 5-agent code review; 144 synth tests passing

import statspai as sp
from statspai.synth.datasets import california_tobacco
df = california_tobacco()

# Run every method at once, pick the best
comp = sp.synth_compare(df, outcome='cigsale', unit='state', time='year',
                        treated_unit='California', treatment_time=1989)
comp.plot()                  # overlay all counterfactuals

# Power analysis before committing to a design
power = sp.synth_power(df, outcome='cigsale', unit='state', time='year',
                       treated_unit='California', treatment_time=1989,
                       effect_sizes=[2, 5, 10, 15, 20])

# One-click publication report
res = sp.synth(df, outcome='cigsale', unit='state', time='year',
               treated_unit='California', treatment_time=1989, method='augmented')
sp.synth_report_to_file(res, 'analysis.md')

See the synth guide for the full 20-method decision table.

Previously in v0.8.0: Spatial Econometrics Full-Stack — 38 new API symbols covering weights, ESDA, ML/GMM regression, GWR/MGWR, and spatial panel. Plus: local projections, GARCH, ARIMA, BVAR, LiNGAM, GES, optimal matching, cardinality matching, RIF decomposition, mediation sensitivity, Cox frailty, AFT survival, rdpower, survey calibration. 60+ new functions across 10 domains.

Built by the team behind CoPaper.AI · Stanford REAP Program

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Why StatsPAI?

Pain point	Stata	R	StatsPAI
Scattered packages	One environment, but $695+/yr license	20+ packages with incompatible APIs	One import, unified API
Publication tables	outreg2 (limited formats)	modelsummary (best-in-class)	Word + Excel + LaTeX + HTML in every function
Robustness checks	Manual re-runs	Manual re-runs	spec_curve() + robustness_report() — one call
Heterogeneity analysis	Manual subgroup splits + forest plots	Manual lapply + ggplot	subgroup_analysis() with Wald test
Modern ML causal	Limited (no DML, no causal forest)	Fragmented (DoubleML, grf, SuperLearner separate)	DML, Causal Forest, Meta-Learners, TMLE, DeepIV
Neural causal models	None	None	TARNet, CFRNet, DragonNet
Causal discovery	None	pcalg (complex API)	notears(), pc_algorithm(), lingam(), ges()
Spatial econometrics	None	5 packages (spdep+spatialreg+sphet+splm+GWmodel)	38 functions: weights→ESDA→ML/GMM→GWR/MGWR→panel
Policy learning	None	policytree (standalone)	policy_tree() + policy_value()
Result objects	Inconsistent across commands	Inconsistent across packages	Unified CausalResult with .summary(), .plot(), .to_latex(), .cite()
Interactive plot editing	Graph Editor (no code export)	None	sp.interactive() — GUI editing with auto-generated code

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What StatsPAI is — and is not

StatsPAI is not a wrapper for R. We independently re-implement every algorithm from the original papers (with citations exposed via .cite()), and for a few mature engines (pyfixest, rdrobust) we use explicit, transparent bindings. What makes StatsPAI different is the unifying layer on top:

• One result object, one API surface. Every estimator — from regress() to callaway_santanna() to causal_forest() to notears() — returns a CausalResult with the same .summary() / .plot() / .to_latex() / .cite() interface. R users juggle 20+ incompatible S3 classes; StatsPAI users juggle one.
• Scope no single R or Python package matches. DID + RD + Synth + Matching + DML + Meta-learners + TMLE + Neural Causal + Causal Discovery + Policy Learning + Conformal + Bunching + Spillover + Matrix Completion — all consistent, all under sp.*.
• Agent-native by design. Self-describing schemas (list_functions(), describe_function(), function_schema()) make StatsPAI the first econometrics toolkit built for LLM-driven research workflows. No other package — in any language — offers this.
• Publication pipeline out of the box. Word + Excel + LaTeX + HTML + Markdown export from every estimator, not a separate modelsummary-style dance.

If a method exists in R, we aim to match or exceed its feature set in Python — and then add what Python can uniquely offer (sklearn integration, JAX/PyTorch backends, agent-native schemas).

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Complete Feature List

Regression Models

Function	Description	Stata equivalent	R equivalent
regress()	OLS with robust/clustered/HAC SE	reg y x, r / vce(cluster c)	fixest::feols()
ivreg()	IV / 2SLS with first-stage diagnostics	ivregress 2sls	fixest::feols() with IV
panel()	Fixed Effects, Random Effects, Between, FD	xtreg, fe / xtreg, re	plm::plm()
heckman()	Heckman selection model	heckman	sampleSelection::selection()
qreg(), sqreg()	Quantile regression	qreg / sqreg	quantreg::rq()
tobit()	Censored regression (Tobit)	tobit	censReg::censReg()
xtabond()	Arellano-Bond dynamic panel GMM	xtabond	plm::pgmm()
glm()	Generalized Linear Model (6 families × 8 links)	glm	stats::glm()
logit(), probit()	Binary choice with marginal effects	logit / probit	stats::glm(family=binomial)
mlogit()	Multinomial logit	mlogit	nnet::multinom()
ologit(), oprobit()	Ordered logit / probit	ologit / oprobit	MASS::polr()
clogit()	Conditional logit (McFadden)	clogit	survival::clogit()
poisson(), nbreg()	Count data (Poisson, Negative Binomial)	poisson / nbreg	MASS::glm.nb()
ppmlhdfe()	Pseudo-Poisson MLE for gravity models	ppmlhdfe	fixest::fepois()
zip_model(), zinb()	Zero-inflated Poisson / NegBin	zip / zinb	pscl::zeroinfl()
hurdle()	Hurdle (two-part) model	—	pscl::hurdle()
truncreg()	Truncated regression (MLE)	truncreg	truncreg::truncreg()
fracreg()	Fractional response (Papke-Wooldridge)	fracreg	—
betareg()	Beta regression	—	betareg::betareg()
liml()	LIML (robust to weak IV)	ivregress liml	AER::ivreg()
jive()	Jackknife IV (many instruments)	—	—
lasso_iv()	LASSO-selected instruments	—	—
feols()	OLS / IV with high-dim fixed effects (pyfixest backend)	reghdfe	fixest::feols()
fepois()	Poisson with high-dim fixed effects	ppmlhdfe	fixest::fepois()
feglm()	GLM with high-dim fixed effects	—	fixest::feglm()
etable()	Publication-quality regression tables (LaTeX/Markdown/HTML)	esttab	fixest::etable()
sureg()	Seemingly Unrelated Regression	sureg	systemfit::systemfit("SUR")
three_sls()	Three-Stage Least Squares	reg3	systemfit::systemfit("3SLS")
biprobit()	Bivariate probit	biprobit	—
etregress()	Endogenous treatment effects	etregress	—
gmm()	General GMM (arbitrary moments)	gmm	gmm::gmm()
frontier()	Stochastic frontier analysis	frontier	sfa::sfa()

Panel Data (Extended)

Function	Description	Stata equivalent
panel_logit(), panel_probit()	Panel binary (FE conditional / RE / CRE Mundlak)	xtlogit / xtprobit
panel_fgls()	FGLS with heteroskedasticity and AR(1)	xtgls
interactive_fe()	Interactive fixed effects (Bai 2009)	—
panel_unitroot()	Panel unit root (IPS / LLC / Fisher / Hadri)	xtunitroot
mixed()	Multilevel / mixed effects (HLM)	mixed

Survival / Duration Analysis

Function	Description	Stata equivalent
cox()	Cox Proportional Hazards	stcox
kaplan_meier()	Kaplan-Meier survival curves	sts graph
survreg()	Parametric AFT (Weibull / exponential / log-normal)	streg
logrank_test()	Log-rank test for group comparison	sts test

Time Series & Cointegration

Function	Description	Stata equivalent
var()	Vector Autoregression	var
granger_causality()	Granger causality test	vargranger
irf()	Impulse response functions	irf graph
structural_break()	Bai-Perron structural break test	estat sbsingle
cusum_test()	CUSUM parameter stability test	—
engle_granger()	Engle-Granger cointegration test	—
johansen()	Johansen cointegration (trace / max-eigenvalue)	vecrank

Nonparametric Methods

Function	Description	Stata equivalent
lpoly()	Local polynomial regression	lpoly
kdensity()	Kernel density estimation	kdensity

Experimental Design & RCT Tools

Function	Description
randomize()	Stratified / cluster / block randomization
balance_check()	Covariate balance with normalized differences
attrition_test()	Differential attrition analysis
attrition_bounds()	Lee / Manski bounds under attrition
optimal_design()	Optimal sample size / cluster design

Missing Data

Function	Description	Stata equivalent
mice()	Multiple Imputation by Chained Equations	mi impute chained
mi_estimate()	Combine estimates via Rubin's rules	mi estimate

Mendelian Randomization

Function	Description
mendelian_randomization()	IVW + MR-Egger + Weighted Median MR
mr_plot()	Scatter plot with MR regression lines

Structural Estimation

Function	Description	Reference
blp()	BLP random-coefficients demand estimation	Berry, Levinsohn & Pakes (1995)

Difference-in-Differences

Function	Description	Reference
did()	Auto-dispatching DID (2×2 or staggered)	—
did_summary()	One-call robustness comparison across CS/SA/BJS/ETWFE/Stacked	—
did_summary_plot()	Forest plot of method-robustness summary	—
did_summary_to_markdown() / _to_latex()	Publication-ready tables from did_summary	—
did_report()	One-call bundle: txt + md + tex + png + json into a folder	—
did_2x2()	Classic two-group, two-period DID	—
callaway_santanna()	Staggered DID with heterogeneous effects	Callaway & Sant'Anna (2021)
sun_abraham()	Interaction-weighted event study	Sun & Abraham (2021)
bacon_decomposition()	TWFE decomposition diagnostic	Goodman-Bacon (2021)
honest_did()	Sensitivity to parallel trends violations	Rambachan & Roth (2023)
continuous_did()	Continuous treatment DID (dose-response)	Callaway, Goodman-Bacon & Sant'Anna (2024)
did_multiplegt()	DID with treatment switching	de Chaisemartin & D'Haultfoeuille (2020)
did_imputation()	Imputation DID estimator	Borusyak, Jaravel & Spiess (2024)
wooldridge_did() / etwfe()	Extended TWFE: xvar= (single/multi) + panel= (repeated CS) + cgroup= (never/notyet)	Wooldridge (2021)
etwfe_emfx()	R etwfe::emfx equivalent — simple/group/event/calendar aggregations	McDermott (2023)
drdid()	Doubly robust 2×2 DID (OR + IPW)	Sant'Anna & Zhao (2020)
stacked_did()	Stacked event-study DID	Cengiz et al. (2019); Baker, Larcker & Wang (2022)
ddd()	Triple-differences (DDD)	Gruber (1994); Olden & Møen (2022)
cic()	Changes-in-changes (quantile DID)	Athey & Imbens (2006)
twfe_decomposition()	Bacon + de Chaisemartin–D'Haultfoeuille weights	Goodman-Bacon (2021); dCDH (2020)
distributional_te()	Distributional treatment effects	Chernozhukov, Fernandez-Val & Melly (2013)
sp.aggte()	Unified aggregation for staggered DID (simple/dynamic/group/calendar) with Mammen multiplier-bootstrap uniform bands	Callaway & Sant'Anna (2021) §4; Mammen (1993)
sp.cs_report()	One-call Callaway–Sant'Anna report: estimation + four aggregations + pre-trend test + Rambachan–Roth breakdown M*	CS2021 + RR2023
sp.ggdid()	aggte() visualiser with uniform-band overlay	mirrors R did::ggdid
CSReport.plot()	2×2 summary figure (event study / θ(g) / θ(t) / RR breakdown)	—
CSReport.to_markdown()	GitHub-Flavoured Markdown export of the full report	—
CSReport.to_latex()	Booktabs LaTeX fragment, jinja2-free	—
CSReport.to_excel()	Six-sheet Excel workbook	—

DiD parity with csdid / differences / R did + HonestDiD

All algorithms below are reimplemented from the original papers — no wrappers, no runtime dependencies on upstream DID packages.

Feature	StatsPAI	csdid (Py)	differences (Py)	R did
Callaway–Sant'Anna ATT(g,t) with DR / IPW / REG	✅	✅	✅	✅
Never-treated / not-yet-treated control group	✅	✅	✅	✅
Anticipation (anticipation=δ)	✅	✅	—	✅
Repeated cross-sections (panel=False)	✅	✅	partial	✅
aggte: simple / dynamic / group / calendar	✅	✅	✅	✅
Mammen multiplier bootstrap, uniform sup-t bands	✅	✅	—	✅
balance_e / min_e / max_e	✅	✅	partial	✅
Sun–Abraham IW with Liang–Zeger cluster SE	✅	—	✅	via fixest::sunab
Borusyak–Jaravel–Spiess imputation + pre-trend Wald	✅	—	—	via didimputation
de Chaisemartin–D'Haultfoeuille switch-on-off	✅	—	—	via DIDmultiplegtDYN
dCDH joint placebo Wald + avg. cumulative effect	✅	—	—	✅ (v2)
Rambachan–Roth sensitivity + breakdown M*	✅	—	—	via HonestDiD
cs ⇄ aggte ⇄ honest_did pipeline (single object)	✅	partial	partial	partial
One-call report card (cs_report)	✅	—	—	via summary()
Markdown / LaTeX / Excel report export	✅	—	—	partial
save_to= one-call bundle (txt + md + tex + xlsx + png)	✅	—	—	—
CSReport.plot() 2×2 summary figure	✅	—	—	—

Regression Discontinuity

Function	Description	Reference
rdrobust()	Sharp/Fuzzy RD with robust bias-corrected inference	Calonico, Cattaneo & Titiunik (2014)
rdplot()	RD visualization with binned scatter	—
rddensity()	McCrary density manipulation test	McCrary (2008)
rdmc()	Multi-cutoff RD	Cattaneo et al. (2024)
rdms()	Geographic / multi-score RD	Keele & Titiunik (2015)
rkd()	Regression Kink Design	Card et al. (2015)

Matching & Reweighting

Function	Description	Stata equivalent
match()	PSM, Mahalanobis, CEM with balance diagnostics	psmatch2 / cem
ebalance()	Entropy balancing	ebalance

Synthetic Control

Function	Description	Reference
synth()	Abadie-Diamond-Hainmueller SCM	Abadie et al. (2010)
sdid()	Synthetic Difference-in-Differences	Arkhangelsky et al. (2021)
Placebo inference, gap plots, weight tables, RMSE plots	—	—

Machine Learning Causal Inference

Function	Description	Reference
dml()	Double/Debiased ML (PLR + IRM) with cross-fitting	Chernozhukov et al. (2018)
causal_forest()	Causal Forest for heterogeneous treatment effects	Wager & Athey (2018)
deepiv()	Deep IV neural network approach	Hartford et al. (2017)
metalearner()	S/T/X/R/DR-Learner for CATE estimation	Kunzel et al. (2019), Kennedy (2023)
tmle()	Targeted Maximum Likelihood Estimation	van der Laan & Rose (2011)
aipw()	Augmented Inverse-Probability Weighting	—

Neural Causal Models

Function	Description	Reference
tarnet()	Treatment-Agnostic Representation Network	Shalit et al. (2017)
cfrnet()	Counterfactual Regression Network	Shalit et al. (2017)
dragonnet()	Dragon Neural Network for CATE	Shi et al. (2019)

Causal Discovery

Function	Description	Reference
notears()	DAG learning via continuous optimization	Zheng et al. (2018)
pc_algorithm()	Constraint-based causal graph learning	Spirtes et al. (2000)

Policy Learning

Function	Description	Reference
policy_tree()	Optimal treatment assignment rules	Athey & Wager (2021)
policy_value()	Policy value evaluation	—

Conformal & Bayesian Causal Inference

Function	Description	Reference
conformal_cate()	Distribution-free prediction intervals for ITE	Lei & Candes (2021)
bcf()	Bayesian Causal Forest (separate mu/tau)	Hahn, Murray & Carvalho (2020)

Dose-Response & Multi-valued Treatment

Function	Description	Reference
dose_response()	Continuous treatment dose-response curve (GPS)	Hirano & Imbens (2004)
multi_treatment()	Multi-valued treatment AIPW	Cattaneo (2010)

Bounds & Partial Identification

Function	Description	Reference
lee_bounds()	Sharp bounds under sample selection	Lee (2009)
manski_bounds()	Worst-case bounds (no assumption / MTR / MTS)	Manski (1990)

Interference & Spillover

Function	Description	Reference
spillover()	Direct + spillover + total effect decomposition	Hudgens & Halloran (2008)

Dynamic Treatment Regimes

Function	Description	Reference
g_estimation()	Multi-stage optimal DTR via G-estimation	Robins (2004)

Bunching & Tax Policy

Function	Description	Reference
bunching()	Kink/notch bunching estimator with elasticity	Kleven & Waseem (2013)

Matrix Completion (Panel)

Function	Description	Reference
mc_panel()	Causal panel data via nuclear-norm matrix completion	Athey et al. (2021)

Other Causal Methods

Function	Description	Stata/R equivalent
causal_impact()	Bayesian structural time-series	R CausalImpact
mediate()	Mediation analysis (ACME/ADE)	medeff / R mediation
bartik()	Shift-share IV with Rotemberg weights	bartik_weight

Post-Estimation

Function	Description	Stata equivalent
margins()	Average marginal effects (AME/MEM)	margins, dydx(*)
marginsplot()	Marginal effects visualization	marginsplot
test()	Wald test for linear restrictions	test x1 = x2
lincom()	Linear combinations with inference	lincom x1 + x2

Diagnostics & Sensitivity

Function	Description	Reference
oster_bounds()	Coefficient stability bounds	Oster (2019)
sensemakr()	Sensitivity to omitted variables	Cinelli & Hazlett (2020)
mccrary_test()	Density discontinuity test	McCrary (2008)
hausman_test()	FE vs RE specification test	Hausman (1978)
anderson_rubin_test()	Weak instrument robust inference + AR confidence set	Anderson & Rubin (1949)
effective_f_test()	Heteroskedasticity-robust effective F (HC1)	Olea & Pflueger (2013)
tF_critical_value()	Adjusted t-ratio critical value (valid under weak IV)	Lee, McCrary, Moreira & Porter (2022, AER)
evalue()	E-value sensitivity to unmeasured confounding	VanderWeele & Ding (2017)
het_test()	Breusch-Pagan / White heteroskedasticity	—
reset_test()	Ramsey RESET specification test	—
vif()	Variance Inflation Factor	—
diagnose()	General model diagnostics	—

Smart Workflow Engine (unique to StatsPAI — no other package has these)

Function	Description
recommend()	Given data + research question → recommends estimators with reasoning, generates workflow, provides .run()
compare_estimators()	Runs multiple methods (OLS, matching, IPW, DML, ...) on same data, reports agreement diagnostics
assumption_audit()	One-call test of ALL assumptions for any method, with pass/fail/remedy for each
sensitivity_dashboard()	Multi-dimensional sensitivity analysis (sample, outliers, unobservables) with stability grade
pub_ready()	Journal-specific publication readiness checklist (Top 5 Econ, AEJ, RCT)
replicate()	Built-in famous datasets (Card 1995, LaLonde 1986, Lee 2008) with replication guides

Robustness Analysis (unique to StatsPAI)

Function	Description	R/Stata equivalent
spec_curve()	Specification Curve / Multiverse Analysis	R specr (limited) / Stata: none
robustness_report()	Automated robustness battery (SE variants, winsorize, trim, add/drop controls, subsamples)	None
subgroup_analysis()	Heterogeneity analysis with forest plot + interaction Wald test	None (manual in both)

Inference Methods

Function	Description
wild_cluster_bootstrap()	Wild cluster bootstrap (Cameron, Gelbach & Miller 2008)
ri_test()	Randomization inference / Fisher exact test

CATE Diagnostics (for Meta-Learners & Causal Forest)

Function	Description
cate_summary(), cate_by_group()	CATE distribution summaries
cate_plot(), cate_group_plot()	CATE visualization
gate_test()	Group Average Treatment Effect test
blp_test()	Best Linear Projection test
compare_metalearners()	Compare S/T/X/R/DR-Learner estimates

Publication-Quality Output

Function	Description	Formats
modelsummary()	Multi-model comparison tables	Text, LaTeX, HTML, Word, Excel, DataFrame
outreg2()	Stata-style regression table export	Excel, LaTeX, Word
sumstats()	Summary statistics (Table 1)	Text, LaTeX, HTML, Word, Excel, DataFrame
balance_table()	Pre-treatment balance check	Text, LaTeX, HTML, Word, Excel, DataFrame
tab()	Cross-tabulation with chi-squared / Fisher	Text, LaTeX, Word, Excel, DataFrame
coefplot()	Coefficient forest plot across models	matplotlib Figure
binscatter()	Binned scatter with residualization	matplotlib Figure
set_theme()	Publication themes ('academic', 'aea', 'minimal', 'cn_journal')	—
interactive()	WYSIWYG plot editor with 29 themes & auto code generation	Jupyter ipywidgets

Every result object has:

result.summary()      # Formatted text summary
result.plot()         # Appropriate visualization
result.to_latex()     # LaTeX table
result.to_docx()      # Word document
result.cite()         # BibTeX citation for the method

Interactive Plot Editor — Python's Answer to Stata Graph Editor

Stata users know the Graph Editor: double-click a figure to enter a WYSIWYG editing interface — drag fonts, change colors, adjust layout. This has been a Stata-exclusive experience. In Python, matplotlib produces static images — changing a title font size means editing code and re-running.

sp.interactive(fig) turns any matplotlib figure into a live editing panel — figure preview on the left, property controls on the right, just like Stata's Graph Editor. But it does two things Stata can't:

1. 29 academic themes, one-click switching. From AER journal style to ggplot, FiveThirtyEight, dark presentation mode — select and see the result instantly. Stata's scheme requires regenerating the plot; here it's real-time.

2. Every edit auto-generates reproducible Python code. Adjust title size, change colors, add annotations in the GUI — the editor records each operation as standard matplotlib code (ax.set_title(...), ax.spines[...].set_visible(...)). Copy with one click, paste into your script, and it reproduces exactly. Stata's Graph Editor cannot export edits to do-file commands.

Five tabs cover all editing needs: Theme (29 themes) · Text (titles, labels, fonts) · Style (line colors, widths, markers) · Layout (spines, grid, figure size, legend, axis limits) · Export (save, undo/redo, reset).

Auto/Manual rendering modes: Auto refreshes the preview on every change; Manual batches edits for a single Apply — useful for large figures or slow machines.

import statspai as sp

result = sp.did(df, y='wage', treat='policy', time='year')
fig, ax = result.plot()
editor = sp.interactive(fig)   # opens the editor

# After editing in the GUI:
editor.copy_code()             # prints reproducible Python code

Utilities

Function	Description	Stata equivalent
label_var(), label_vars()	Variable labeling	label var
describe()	Data description	describe
pwcorr()	Pairwise correlation with significance stars	pwcorr, star(.05)
winsor()	Winsorization	winsor2
read_data()	Multi-format data reader	use / import

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Installation

pip install statspai

With optional dependencies:

pip install statspai[plotting]    # matplotlib, seaborn
pip install statspai[fixest]      # pyfixest for high-dimensional FE

Requirements: Python >= 3.9

Core dependencies: NumPy, SciPy, Pandas, statsmodels, scikit-learn, linearmodels, patsy, openpyxl, python-docx

---

Quick Example

import statspai as sp

# --- Estimation ---
r1 = sp.regress("wage ~ education + experience", data=df, robust='hc1')
r2 = sp.ivreg("wage ~ (education ~ parent_edu) + experience", data=df)
r3 = sp.did(df, y='wage', treat='policy', time='year', id='worker')
r4 = sp.rdrobust(df, y='score', x='running_var', c=0)
r5 = sp.dml(df, y='wage', treat='training', covariates=['age', 'edu', 'exp'])
r6 = sp.causal_forest("y ~ treatment | x1 + x2 + x3", data=df)

# --- Post-estimation ---
sp.margins(r1, data=df)              # Marginal effects
sp.test(r1, "education = experience") # Wald test
sp.oster_bounds(df, y='wage', treat='education', controls=['experience'])

# --- Tables (to Word / Excel / LaTeX) ---
sp.modelsummary(r1, r2, output='table2.docx')
sp.outreg2(r1, r2, r3, filename='results.xlsx')
sp.sumstats(df, vars=['wage', 'education', 'age'], output='table1.docx')

# --- Robustness (unique to StatsPAI) ---
sp.spec_curve(df, y='wage', x='education',
              controls=[[], ['experience'], ['experience', 'female']],
              se_types=['nonrobust', 'hc1']).plot()

sp.robustness_report(df, formula="wage ~ education + experience",
                     x='education', extra_controls=['female'],
                     winsor_levels=[0.01, 0.05]).plot()

sp.subgroup_analysis(df, formula="wage ~ education + experience",
                     x='education',
                     by={'Gender': 'female', 'Region': 'region'}).plot()

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StatsPAI vs Stata vs R: Honest Comparison

Where StatsPAI wins

Advantage	Detail
Unified API	One package, one import, consistent .summary() / .plot() / .to_latex() across all methods. Stata requires paid add-ons; R requires 20+ packages with different interfaces.
Modern ML causal methods	DML, Causal Forest, Meta-Learners (S/T/X/R/DR), TMLE, DeepIV, TARNet/CFRNet/DragonNet, Policy Trees — all in one place. Stata has almost none of these. R has them scattered across incompatible packages.
Robustness automation	spec_curve(), robustness_report(), subgroup_analysis() — no manual re-running. Neither Stata nor R offers this out-of-the-box.
Free & open source	MIT license, $0. Stata costs $695–$1,595/year.
Python ecosystem	Integrates naturally with pandas, scikit-learn, PyTorch, Jupyter, cloud pipelines.
Auto-citations	Every causal method has .cite() returning the correct BibTeX. Neither Stata nor R does this.
Interactive Plot Editor	sp.interactive() — Stata Graph Editor-style GUI in Jupyter with 29 themes and auto-generated reproducible code. Stata's Graph Editor can't export edits to do-file; R has no equivalent.

Where Stata still wins

Advantage	Detail
Battle-tested at scale	40+ years of production use in economics. Edge cases are well-handled.
Speed on very large datasets	Stata's compiled C backend is faster for simple OLS/FE on datasets with millions of rows.
Survey data & complex designs	svy: prefix, stratification, clustering — Stata's survey support is unmatched.
Mature documentation	Every command has a PDF manual with worked examples. Community is massive.
Journal acceptance	Referees in some fields trust Stata output by default.

Where R still wins

Advantage	Detail
Cutting-edge methods	New econometric methods (e.g., fixest, did2s, HonestDiD) often appear in R first.
ggplot2 visualization	R's grammar of graphics is more flexible than matplotlib for complex figures.
modelsummary	R's modelsummary is the gold standard for regression tables — StatsPAI's is close but not yet identical.
CRAN quality control	R packages go through peer review. Python packages vary in quality.
Spatial econometrics	spdep, spatialreg — As of v0.8.0, StatsPAI matches R's 5-package spatial stack (spdep + spatialreg + sphet + splm + GWmodel) in a single unified API, with numerical parity to PySAL spreg at rtol<1e-7 on the Columbus benchmark.

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API at a Glance

450+ public functions/classes

Regression:     regress, ivreg, glm, logit, probit, mlogit, ologit, poisson, nbreg, ppmlhdfe,
                tobit, heckman, qreg, truncreg, fracreg, betareg, sureg, three_sls, gmm
IV Advanced:    liml, jive, lasso_iv
Panel:          panel, panel_logit, panel_probit, panel_fgls, interactive_fe, xtabond, mixed
DID:            did, callaway_santanna, sun_abraham, bacon_decomposition, honest_did,
                continuous_did, did_multiplegt, did_imputation, stacked_did
RD:             rdrobust, rdplot, rddensity, rdmc, rdms, rkd
Matching:       match, ebalance, ipw, aipw
Synth:          synth, sdid, gsynth, augsynth, staggered_synth, conformal_synth
ML Causal:      dml, causal_forest, deepiv, metalearner, tmle
Neural:         tarnet, cfrnet, dragonnet
Spatial:        sar, sem, sdm, slx, sac, sar_gmm, sem_gmm, sarar_gmm,          ← NEW v0.8
                moran, geary, getis_ord_g, join_counts, lm_tests, impacts,
                gwr, mgwr, gwr_bandwidth, spatial_panel,
                queen_weights, rook_weights, knn_weights, distance_band, kernel_weights
Discovery:      notears, pc_algorithm, lingam, ges                              ← NEW v0.8
Policy:         policy_tree, policy_value
Survival:       cox, kaplan_meier, survreg, logrank_test, cox_frailty, aft      ← NEW v0.8
Time Series:    var, granger_causality, irf, structural_break, johansen,
                local_projections, garch, arima, bvar                           ← NEW v0.8
Nonparametric:  lpoly, kdensity
Experimental:   randomize, balance_check, attrition_test, optimal_design
Matching:       match, ebalance, optimal_match, cardinality_match               ← NEW v0.8
Decomposition:  oaxaca, gelbach, rifreg, rif_decomposition                      ← NEW v0.8
Imputation:     mice, mi_estimate
Frontier:       frontier (stochastic frontier analysis)
Structural:     blp (BLP demand estimation)
Survey:         svydesign, svymean, svytotal, svyglm, rake, linear_calibration  ← NEW v0.8
MR:             mendelian_randomization, mr_ivw, mr_egger, mr_median
Mediation:      mediate, mediate_sensitivity                                    ← NEW v0.8
RD:             rdrobust, rdplot, rddensity, rdmc, rdms, rdpower, rdsampsi      ← NEW v0.8
Smart Workflow: recommend, compare_estimators, assumption_audit,
                sensitivity_dashboard, pub_ready, replicate
Output:         modelsummary, outreg2, sumstats, balance_table, tab, coefplot, binscatter
Plot Editor:    interactive (WYSIWYG editor), set_theme (29 academic themes)

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Release Notes

v0.8.0 (2026-04-16) — Spatial Econometrics Full-Stack + 10-Domain Breadth Upgrade

60+ new functions, 450+ total API, 1,230+ tests passing. Largest release in StatsPAI history.

Spatial Econometrics (NEW — 38 API symbols, 3,178 LOC, 69 tests):

• Weights: W (sparse CSR), queen_weights, rook_weights, knn_weights, distance_band, kernel_weights, block_weights
• ESDA: moran (global + local LISA), geary, getis_ord_g, getis_ord_local, join_counts, moran_plot, lisa_cluster_map
• ML Regression: sar, sem, sdm, slx, sac (SARAR) — sparse-aware, scales to N=100K
• GMM Regression: sar_gmm, sem_gmm, sarar_gmm (Kelejian-Prucha 1998/1999, het-robust)
• Diagnostics: lm_tests (Anselin 1988 — LM-err/LM-lag/Robust-LM/SARMA), moran_residuals
• Effects: impacts (LeSage-Pace 2009 direct/indirect/total with simulated SE)
• GWR: gwr, mgwr (Multiscale GWR), gwr_bandwidth (AICc/CV golden-section)
• Spatial Panel: spatial_panel (SAR-FE / SEM-FE / SDM-FE, entity + twoways)
• Cross-validated: Columbus SAR/SEM rtol<1e-7 vs PySAL spreg; Georgia GWR bit-identical vs mgwr

Time Series (4 new estimators):

• local_projections — Jordà (2005) horizon-by-horizon IRF with Newey-West HAC
• garch — GARCH(p,q) volatility model, MLE, multi-step forecast
• arima — ARIMA/SARIMAX with auto (p,d,q) selection via AICc grid
• bvar — Bayesian VAR with Minnesota (Litterman) prior, closed-form posterior

Causal Discovery (2 new algorithms):

• lingam — DirectLiNGAM (Shimizu 2011), bit-identical match vs lingam package
• ges — Greedy Equivalence Search (Chickering 2002), BIC-based CPDAG learning

Matching (2 new methods):

• optimal_match — global 1:1 matching via Hungarian algorithm (min total Mahalanobis distance)
• cardinality_match — Zubizarreta (2014) LP-based matching with SMD balance constraints

Decomposition & Mediation:

• rifreg — RIF regression (Firpo-Fortin-Lemieux 2009), unconditional quantile partial effects
• rif_decomposition — RIF Oaxaca-Blinder for distributional statistics
• mediate_sensitivity — Imai-Keele-Yamamoto (2010) ρ-sensitivity analysis

RD & Design:

• rdpower, rdsampsi — power calculations for RD designs (Cattaneo et al. 2019)

Survey:

• rake — iterative proportional fitting (Deming-Stephan) for survey calibration
• linear_calibration — Deville-Särndal (1992) chi-squared distance calibration

Survival (2 new models):

• cox_frailty — Cox proportional hazards with shared gamma frailty
• aft — Accelerated Failure Time (exponential/Weibull/lognormal/loglogistic)

ML-Causal (GRF extensions):

• CausalForest.variable_importance() — permutation-based feature importance for CATE
• CausalForest.best_linear_projection() — BLP heterogeneity test (Chernozhukov et al. 2020)
• CausalForest.ate(), .att() — convenience accessors
• Bugfix: honest leaf values now correctly vary per-leaf (was overwriting all leaves)

Infrastructure:

• OLS/IV predict(data, what='confidence'|'prediction') — out-of-sample with intervals
• Pre-release code review: 3 critical + 2 high-priority bugs fixed before release

v0.6.0 (2026-04-05) — Complete Econometrics Toolkit + Smart Workflow Engine

30 new modules, 390+ public API, 860+ tests passing, 83K+ lines of code.

New Regression & GLM:

• glm() (6 families × 8 links), logit(), probit(), cloglog(), mlogit(), ologit(), oprobit(), clogit()
• poisson(), nbreg(), ppmlhdfe() (gravity model), zip_model(), zinb(), hurdle()
• truncreg(), fracreg(), betareg(), biprobit(), etregress()
• liml(), jive(), lasso_iv() (advanced IV), sureg(), three_sls(), gmm() (general GMM)

New Panel & Multilevel:

• panel_logit(), panel_probit() (FE/RE/CRE), panel_fgls(), interactive_fe() (Bai 2009)
• panel_unitroot() (IPS/LLC/Fisher/Hadri), mixed() (multilevel/HLM)

New Survival: cox(), kaplan_meier(), survreg(), logrank_test()

New Time Series: var(), granger_causality(), irf(), structural_break(), cusum_test(), engle_granger(), johansen()

New Causal: continuous_did(), rdmc(), rdms() (geographic RD), distributional_te(), mendelian_randomization()

New Design & Data: randomize(), balance_check(), attrition_test(), optimal_design(), mice(), mi_estimate()

New Structural: blp() (BLP demand estimation), frontier() (stochastic frontier)

Smart Workflow Engine (unique to StatsPAI):

• recommend() — data + question → estimator recommendation + workflow
• compare_estimators() — multi-method comparison with agreement diagnostics
• assumption_audit() — one-call assumption testing with remedies
• sensitivity_dashboard() — multi-dimensional sensitivity analysis
• pub_ready() — journal-specific publication readiness checklist
• replicate() — built-in famous datasets with replication guides

Interactive Plot Editor: Font presets redesigned to show actual font names; separate font and size presets for independent per-element control.

v0.6.2 (2026-04-12) — Weights, Prediction & Validation

• OLS predict(): Out-of-sample prediction via result.predict(newdata=)
• balance_panel(): Keep only units observed in every period
• DID/DDD/Event Study weights: weights= parameter for population-weighted estimation
• Matching ps_poly=: Polynomial propensity score models (Cunningham 2021, Ch. 5)
• Synth RMSPE plot: synthplot(result, type='rmspe') histogram (Abadie et al. 2010)
• Graddy (2006) replication: Fulton Fish Market IV example in sp.replicate()
• Numerical validation: Cross-validated against Stata/R reference values

v0.6.1 (2026-04-07) — Interactive Editor Fixes & Improvements

• Theme switching fix: Themes now fully reset rcParams before applying, so switching between themes (e.g. ggplot → academic) correctly updates all visual properties
• Apply button fix: Fixed being clipped on the Layout tab; now pinned to panel bottom
• Error visibility: Widget callback errors now surface in the status bar instead of being silently swallowed
• Auto mode: Always refreshes preview when toggled for immediate feedback
• Theme tab: Moved to first position; color pickers show confirmation feedback
• Code generation: Auto-generate reproducible code with text selection support

v0.5.1 (2026-04-04) — Interactive Plot Editor & Agent Enhancements

v0.4.0 (2026-04-05) — Module Architecture Overhaul

Major refactoring and expansion of core modules (+5,800 lines of new code):

• DID: Added Triple Differences (ddd()), one-call did_analysis() workflow (auto design detection → Bacon decomposition → estimation → event study → sensitivity), and 8 publication-ready plot functions (parallel_trends_plot, bacon_plot, group_time_plot, enhanced_event_study_plot, treatment_rollout_plot, sensitivity_plot, cohort_event_study_plot)
• Synthetic Control: Modular rewrite — demeaned_synth(), robust_synth() (penalized SCM), gsynth() (Generalized SCM with interactive fixed effects), staggered_synth() (multi-unit staggered adoption), conformal_synth() (distribution-free inference), and comprehensive synth_plot() / synth_weight_plot() / synth_gap_plot()
• Panel: Major expansion of panel() — Hausman test, Breusch-Pagan LM, Pesaran CD, Wooldridge autocorrelation, panel unit root tests; added panel_summary_plot(), fe_plot(), re_comparison_plot()
• RD: New rd_diagnostics() suite — bandwidth sensitivity, placebo cutoffs, donut-hole robustness, covariate balance at cutoff, density test
• IV / 2SLS: Rewritten ivreg() with proper first-stage diagnostics (Cragg-Donald, Kleibergen-Paap), weak IV detection, Sargan-Hansen overidentification test, Anderson canonical correlation test, Stock-Yogo critical values
• Matching: Enhanced match() — added CEM (Coarsened Exact Matching), optimal matching, genetic matching; improved balance diagnostics with Love plot and standardized mean difference
• DAG: Expanded dag() with 15+ built-in example DAGs (dag_example()), dag_simulate() for data generation from causal graphs, backdoor/frontdoor criterion identification
• Causal Impact: Enhanced Bayesian structural time-series with automatic model selection and improved inference
• AI Agent Registry: Expanded list_functions(), describe_function(), function_schema(), search_functions() for LLM/agent tool-use integration
• CausalResult: Added .to_json(), .to_dict(), enhanced .summary() formatting

v0.3.1 (2025-12-20)

• Fix PyPI badge displaying stale version

v0.3.0 (2025-12-20) — ML & Advanced Causal Methods

• Meta-Learners: S/T/X/R/DR-Learner for CATE estimation with compare_metalearners() and CATE diagnostics (gate_test, blp_test)
• Neural Causal Models: TARNet, CFRNet, DragonNet for deep CATE estimation
• Causal Discovery: notears() (continuous DAG optimization), pc_algorithm() (constraint-based)
• TMLE: Targeted Maximum Likelihood Estimation with Super Learner
• Policy Learning: policy_tree() optimal treatment rules, policy_value() evaluation
• Conformal Causal: Distribution-free prediction intervals for ITE
• Bayesian Causal Forest: bcf() with separate prognostic/treatment functions
• Dose-Response: Continuous treatment GPS curves
• Bounds: Lee bounds (sample selection), Manski bounds (partial identification)
• Interference: spillover() direct + indirect effect decomposition
• DTR: g_estimation() multi-stage optimal treatment regimes
• Multi-Treatment: AIPW for multi-valued treatments
• Bunching: Kink/notch bunching estimator with elasticity
• Matrix Completion: mc_panel() nuclear-norm panel estimator
• Robustness: spec_curve(), robustness_report(), subgroup_analysis()
• New Regression: DeepIV, Heckman selection, quantile regression, Tobit, Arellano-Bond GMM
• New Diagnostics: E-value, Anderson-Rubin weak IV test, Sensemakr, RD density test
• Other: Entropy balancing, Sun-Abraham event study, Bacon decomposition, HonestDiD

v0.2.0 (2025-11-15) — Post-Estimation & Output

• Post-Estimation: margins(), marginsplot(), test(), lincom()
• Output Tables: modelsummary(), outreg2(), sumstats(), balance_table(), tab(), coefplot(), binscatter()
• Inference: wild_cluster_bootstrap(), aipw(), ri_test()
• New Modules: DML, Causal Forest, Matching (PSM/Mahalanobis), Synthetic Control (ADH + SDID), Panel (FE/RE/FD), Causal Impact, Mediation, Bartik IV
• Diagnostics: oster_bounds(), mccrary_test(), hausman_test(), het_test(), reset_test(), vif()
• Utilities: Variable labeling, describe(), pwcorr(), winsor(), read_data()

v0.1.0 (2025-10-01) — Initial Release

• Core regression: regress() OLS with robust/clustered/HAC standard errors
• Instrumental variables: ivreg() 2SLS
• Difference-in-Differences: did(), did_2x2(), callaway_santanna()
• Regression discontinuity: rdrobust()
• Unified CausalResult object with .summary(), .plot(), .to_latex(), .to_docx(), .cite()

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About

StatsPAI Inc. is the research infrastructure company behind CoPaper.AI — the AI co-authoring platform for empirical research, born out of Stanford's REAP program.

CoPaper.AI — Upload your data, set your research question, and produce a fully reproducible academic paper with code, tables, and formatted output. Powered by StatsPAI under the hood. copaper.ai

Team:

• Bryce Wang — Founder. Economics, Finance, CS & AI. Stanford REAP.
• Dr. Scott Rozelle — Co-founder & Strategic Advisor. Stanford Senior Fellow, author of Invisible China.
• 

---

Contributing

git clone https://github.com/brycewang-stanford/statspai.git
cd statspai
pip install -e ".[dev,plotting,fixest]"
pytest

---

Citation

@software{wang2025statspai,
  title={StatsPAI: The Causal Inference & Econometrics Toolkit for Python},
  author={Wang, Bryce},
  year={2025},
  url={https://github.com/brycewang-stanford/statspai},
  version={0.6.0}
}

License

MIT License. See LICENSE.

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GitHub · PyPI · User Guide · CoPaper.AI