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

StatsPAI is the agent-native Python package for causal inference and applied econometrics: one import statspai as sp, 900+ functions behind a self-describing API, and CausalResult objects that export to LaTeX / Word / Excel / BibTeX.

This skill drives StatsPAI through the canonical pipeline of an applied AER empirical paper. Each step maps to a section of the published paper and 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 (>= 1.6)
• Paper: submitted to JOSS (under review)

Why for Agents

1. Self-describing: sp.list_functions() / sp.describe_function(name) / sp.function_schema(name) — every public symbol is discoverable without doc lookup.
2. Unified result: every estimator returns CausalResult with .summary(), .plot(), .diagnostics, .to_latex(), .to_word(), .cite().
3. One import, full pipeline: data contract → Table 1 → estimand-first DSL → identification graphs → main table → heterogeneity → mechanisms → robustness → replication package.
4. Estimand-first: sp.causal_question(...).identify() forces the "DID vs RD vs IV?" decision before estimation, with the identifying assumption written down — the way a referee expects to read it.

The AER-style empirical pipeline