StatsPAI: Agent-Native Causal Inference & AER-Style Empirical Workflow

StatsPAI is a validation-tiered Python package for causal inference and applied econometrics: one import statspai as sp, 1,100+ registered functions behind a self-describing API, and mature estimator result objects that commonly export to LaTeX / Word / Excel / BibTeX.

This skill drives StatsPAI through the canonical pipeline of an applied AER empirical paper. Each step emits a paper-ready artifact (Table 1, event-study figure, Table 2 main results, robustness panel, replication stamp).

• Source: https://github.com/brycewang-stanford/StatsPAI
• Install: pip install "statspai[fixest,plotting]" (API surface re-validated against statspai 1.19.0 — every sp.* reference, signature, and result-object attribute claim in this skill is checked by validate_api_claims.py in this folder). The bare pip install statspai is not enough for the default pipeline — see the dependency matrix below.
• Paper: JOSS submission under review; JSS materials in Paper-JSS/README.md and docs/jss_source_audit_dossier.md