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](https://github.com/brycewang-stanford/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`, `...