aer-statspai

name	aer-statspai
description	Use when aer-identification has fixed the design, after methodology choice and before aer-robustness or aer-tables-figures, to run an AER-track analysis with StatsPAI — the agent-native Python engine and MCP server for causal inference, robustness, sensitivity, and publication-ready table export.

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.
2. 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.
3. Fit with as_handle=true — e.g. callaway_santanna, ivreg, rdrobust, synth. You get a result_id you can chain downstream.
4. 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.
5. honest_did_from_result / sensitivity_from_result — design-specific sensitivity (Rambachan-Roth honest bounds, Oster δ, etc.) directly off the handle.
6. 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, did_multiplegt, lp_did
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
Factor-model counterfactuals	Interactive FE / matrix completion for larger treated blocks	interactive_fe, matrix_completion
Bunching / kink designs	Excess mass over a polynomial counterfactual	bunching, notch
DML / causal ML	Cross-fit nuisance; honest CIs	dml, causal_forest, metalearner, tmle, aipw
Distributional effects	Quantile treatment effects when the mean hides the action	qte
Robustness / few-cluster inference	Specification curve; cluster-robust variance; exact randomization tests	spec_curve, wild_cluster_bootstrap, twoway_cluster, conley, ri_test
Sensitivity to confounding / multiplicity	Oster δ-R²; Cinelli-Hazlett robustness value; FWER control	oster_delta, oster_bounds, robustness_value, romano_wolf
Partial identification under attrition	Lee (2009) trimming bounds when selection is differential	lee_bounds

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
• robustness_value — Cinelli-Hazlett (2020) partial-R² robustness value and bias factor
• lee_bounds — Lee (2009) trimming bounds under differential attrition
• 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: <DiD | IV | RDD | SCM | DML | ...>
ESTIMATOR FUNCTION: <statspai function used>
VALIDATION STATUS: <certified | api-stable — cross-checked? yes/no>
AUDIT_RESULT CHECKS CLOSED: <list or "none">
SENSITIVITY RUN: <honest_did | oster | evalue | none>
TABLES EXPORTED: <paths or "none">
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