Aer Statspai

AER StatsPAI

Overview

aer-statspai is the implementation engine option for this stack. Where aer-identification and aer-robustness decide which estimator and which diagnostics a referee will demand, this skill is about running them — through StatsPAI, an open-source, agent-native Python platform that exposes 1,000+ causal-inference and econometrics functions behind one unified API, plus a machine-readable MCP server an agent can drive directly.

It is one more choice, not a replacement. The Stata / R / Python templates in templates/ remain the default for users who want drop-in, version-pinned scripts. Reach for StatsPAI when you want a single Python surface that covers the whole AER identification toolkit, self-describes its assumptions to an agent, and exports publication-ready LaTeX / Word / Excel tables from the estimator object.

This skill does not override the methodology. The modern-default rules in aer-identification (no TWFE on staggered data, Anderson-Rubin under weak IV, local-linear RDD, placebo inference for SCM) still bind. StatsPAI executes those rules; it does not relax them.

When to Use

• You want to run the empirical analysis interactively from the agent, not

just receive template code to run later by hand

• You want one Python dependency covering DiD, IV, RDD, SCM, matching, DML, and

causal forests instead of stitching together fixest, did, rdrobust, linearmodels, and scdata

• A StatsPAI MCP server is connected (tools prefixed statspai) and you can

chain detect_design → recommend → fit → audit_result → sensitivity → bibtex

• You need publication-ready tables straight from the estimator object

(.to_latex(), .to_docx(), .to_excel()) to hand to aer-tables-figures

When NOT to Use

• The deposited replication package must be Stata-only (some coauthors /

Data Editor workflows assume .do files) → use templates/stata/

• You want exact, pinned, language-native scripts to commit verbatim into an

openICPSR deposit → use templates/python/, templates/r/, or templates/stata/

• The methodological choice is the open question — go to aer-identification

first; come back here to execute once the design is fixed

Install and Connect

Two ways to use StatsPAI; they share the same estimators.

pip install statspai

import statspai as sp

df = sp.datasets.mpdta()                       # bundled teaching panel
cs = sp.callaway_santanna(data=df, y="lemp", t="year",
                          i="countyreal", g="first_treat")
print(sp.aggte(cs, type="simple").summary())   # staggered-robust ATT

Agent-native (MCP) path. When a StatsPAI MCP server is connected, the agent calls the same methods as tools (statspai namespace) and gets back structured result handles instead of parsing console output. This is the preferred path inside an agent loop because every function publishes its assumptions, preconditions, and failure modes for the agent to read before it runs.

Recommended Agent-Native Workflow

Drive the MCP server as a chain, not as one-shot calls. Pass as_handle=true (or detail='agent') so each step returns a result_id the next step consumes — no need to ferry betas, standard errors, or covariance matrices by hand.

1. detect_design — infer the study shape (DiD / IV / RDD / SCM / panel), or

pass design= explicitly when you already know it.

1. preflight + recommend — surface design problems (no never-treated

group, weak first stage, manipulation at the cutoff) and let the engine propose the modern-default estimator.

1. Fit with as_handle=true — e.g. callaway_santanna, ivreg,

rdrobust, synth. You get a result_id you can chain downstream.

1. audit_result(result_id=...) — enumerate the robustness checks still

missing; for each, call the suggest_function it emits. This is the referee-anticipation step from aer-robustness, automated.

1. honest_did_from_result / sensitivity_from_result — design-specific

sensitivity (Rambachan-Roth honest bounds, Oster δ, etc.) directly off the handle.

1. bibtex(keys=[...]) — pull verified citations for every estimator and

diagnostic you used. Never invent references — paper.bib is the single source of truth.

Token economy: pass detail='minimal' on cheap sub-step calls; the default detail='agent' carries the violations list and next_steps you actually need.

Worked Execution Snapshot

Before handing results to aer-robustness, leave this shape:

DESIGN: staggered DiD; n = 48,212; cohorts = 37
RESULT: ATT = -0.042; SE = 0.011; pretrend p = 0.64
AUDIT: forbidden weight = 0.31; honest-DiD Mbar=1 bound [-0.071, -0.009]
DECISION: advance only if template cross-check agrees within 0.002

Mapping AER Identification Strategies to StatsPAI

Each row keeps the modern default from aer-identification; StatsPAI is the execution surface.

| Strategy | Modern default (see aer-identification) | StatsPAI entry point | |---|---|---| | Staggered DiD | Callaway-Sant'Anna ATT(g,t); never raw TWFE | callaway_santanna, aggte, did_imputation, sun_abraham | | Forbidden-comparison check | Goodman-Bacon decomposition | bacon_decomposition, bacon_plot | | Event study / pre-trends | Joint pre-period test, not just the plot | event_study, pretrends_test, honest_did | | IV / weak instruments | Anderson-Rubin, not first-stage F > 10 | ivreg, anderson_rubin_ci, effective_f_test, tF_adjustment | | Shift-share / Bartik | Rotemberg weights or shock-level inference | bartik | | RDD | Local-linear, MSE-optimal bandwidth, RBC CI | rdrobust, rdbwselect, rdplot, rddensity (McCrary) | | Synthetic control | Placebo inference; modern variants | synth, gsynth, augsynth, sdid, synth_time_placebo, synth_loo | | DML / causal ML | Cross-fit nuisance; honest CIs | dml, causal_forest, metalearner, tmle |

When in doubt about whether an estimator is appropriate, that decision belongs to aer-identification. This table is for how to run the one you've chosen.

Robustness, Heterogeneity, Sensitivity

audit_result is the bridge to aer-robustness: it reads the fitted handle and lists the checks a referee will expect — placebo, alternative samples, heterogeneity by cohort, leave-one-out for SCM — emitting a suggest_function for each. Drive that list to closure rather than guessing which robustness checks to add.

For design-specific sensitivity, prefer the *_from_result tools so you never re-specify the model:

• honest_did_from_result — Rambachan-Roth (2023) honest bounds for DiD
• sensitivity_from_result — Oster (2019) δ / unobserved-confounding bounds
• evalue_from_result — E-value for observational designs

Publication Export — Hand Off to aer-tables-figures

StatsPAI result objects export directly, which removes manual table assembly:

res.to_latex("output/tables/table3.tex")   # AER booktabs-compatible
res.to_docx("output/tables/table3.docx")
res.to_excel("output/tables/table3.xlsx")

The house-style rules still come from aer-tables-figures — column count, note structure, significance-star policy, and the booktabs conventions. Use StatsPAI to emit the table; use aer-tables-figures to decide what the table should look like before you ship it.

Validation Status — Read It Before Trusting a Number

StatsPAI labels every function with a validation_status. Treat these as a trust tier, not decoration:

• Certified numerical evidence (benchmarked against Stata / R reference

implementations) → safe for a main specification.

• API-stable breadth (broad coverage, not yet numerically certified

end-to-end) → fine for exploration; re-run the headline result against a reference implementation (templates/) before it becomes a main table in a top-5 submission.

For an AER main result, cross-check at least the headline coefficient and its inference against the language-native template. Convergent numbers across two engines is itself a robustness signal a referee will respect.

Red Flags

• Reporting a StatsPAI estimate as a main AER specification without confirming

its validation_status is certified or cross-checking against templates/

• Letting the unified API tempt you into a worse estimator than

aer-identification prescribes (e.g. plain TWFE because it is one call)

• Skipping audit_result and hand-picking robustness checks — the whole point

of the agent-native loop is that it enumerates what you missed

• Pasting a citation StatsPAI did not return from bibtex — verified keys only

Repository Resources

When working from the AER-skills repository or plugin bundle, load only the relevant resource:

• Estimator defaults, diagnostics, and primary citations: docs/methods-reference.md
• The methodological decision that precedes execution: skills/aer-identification/SKILL.md
• Referee-anticipating checks audit_result should reproduce: skills/aer-robustness/SKILL.md
• House-style rules for the tables StatsPAI emits: skills/aer-tables-figures/SKILL.md
• Reference Python implementation to cross-check headline numbers: templates/python/main_did.py
• Pinned reference-engine dependencies: templates/python/requirements.txt

Use aer-identification to fix the design before running anything here; use docs/methods-reference.md to confirm the estimand, diagnostic, and citation the manuscript must report.

Handoff

ENGINE: StatsPAI (python-api | mcp)
DESIGN: 
ESTIMATOR FUNCTION: 
VALIDATION STATUS: 
AUDIT_RESULT CHECKS CLOSED: 
SENSITIVITY RUN: 
TABLES EXPORTED: 
NEXT SKILL: aer-robustness (close audit) → aer-tables-figures (house style)

Anti-Patterns

• Treating aer-statspai as a substitute for aer-identification — it executes

the design, it does not choose it

• Shipping an api-stable result as a main top-5 table with no reference

cross-check

• Using the unified API to add a fifth "robustness" estimator that shares the

same identifying variation as the main result and calling it confirmation

• Exporting a table from StatsPAI and skipping aer-tables-figures — the engine

emits a table, it does not enforce AER house style

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: brycewang-stanford
• Source: brycewang-stanford/AER-Skills
• License: MIT

Install and usage instructions live in the source repository linked above.