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 v0.6: sp.interactive(fig) — a Stata Graph Editor-style WYSIWYG plot editor for Jupyter, with 29 academic themes, real-time preview, and auto-generated reproducible code.

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

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()`
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

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	—	—
`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_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)
`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	Anderson & Rubin (1949)
`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:

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.
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`

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()

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` — R has a deeper spatial ecosystem.

API at a Glance

390+ 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
Discovery:      notears, pc_algorithm
Policy:         policy_tree, policy_value
Survival:       cox, kaplan_meier, survreg, logrank_test
Time Series:    var, granger_causality, irf, structural_break, johansen, engle_granger
Nonparametric:  lpoly, kdensity
Experimental:   randomize, balance_check, attrition_test, optimal_design
Imputation:     mice, mi_estimate
Frontier:       frontier (stochastic frontier analysis)
Structural:     blp (BLP demand estimation)
MR:             mendelian_randomization, mr_ivw, mr_egger, mr_median
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)

Release Notes

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()

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.

GitHub · PyPI · User Guide · CoPaper.AI

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.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CHANGELOG.md		CHANGELOG.md
CHANGELOG_GITHUB.md		CHANGELOG_GITHUB.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
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paper.bib		paper.bib
paper.md		paper.md
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StatsPAI: The Agent-Native Causal Inference & Econometrics Toolkit for Python

Why StatsPAI?

Complete Feature List

Regression Models

Panel Data (Extended)

Survival / Duration Analysis

Time Series & Cointegration

Nonparametric Methods

Experimental Design & RCT Tools

Missing Data

Mendelian Randomization

Structural Estimation

Difference-in-Differences

DiD parity with csdid / differences / R did + HonestDiD

Regression Discontinuity

Matching & Reweighting

Synthetic Control

Machine Learning Causal Inference

Neural Causal Models

Causal Discovery

Policy Learning

Conformal & Bayesian Causal Inference

Dose-Response & Multi-valued Treatment

Bounds & Partial Identification

Interference & Spillover

Dynamic Treatment Regimes

Bunching & Tax Policy

Matrix Completion (Panel)

Other Causal Methods

Post-Estimation

Diagnostics & Sensitivity

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

Robustness Analysis (unique to StatsPAI)

Inference Methods

CATE Diagnostics (for Meta-Learners & Causal Forest)

Publication-Quality Output

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

Utilities

Installation

Quick Example

StatsPAI vs Stata vs R: Honest Comparison

Where StatsPAI wins

Where Stata still wins

Where R still wins

API at a Glance

Release Notes

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

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

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

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

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

v0.3.1 (2025-12-20)

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

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

v0.1.0 (2025-10-01) — Initial Release

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DiD parity with `csdid` / `differences` / R `did` + `HonestDiD`

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