statspai-analysis

# StatsPAI: agent-native causal inference, paper-ready StatsPAI is one `import statspai as sp` over 1,200+ registered functions for causal inference and applied econometrics, numerically aligned with Stata / R reference implementations, with self-describing schemas and result objects that export to LaTeX / Word / Excel / BibTeX. This skill drives it through the canonical pipeline of an applied empirical paper, in one of three modes (default applied econ; epidemiology; ML-causal), and leaves paper-ready artifacts on disk at every step. - **Source**: https://github.com/brycewang-stanford/StatsPAI - **Install**: `pip install "statspai[fixest,plotting]"` (default pipeline); add `neural` for Dragonnet / TARNet / CEVAE, `text` for text-as-treatment. The bare install cannot run `sp.feols` (any `y ~ x | fe` regression) or make figures — see `references/operating-loop.md` for the extras matrix. - **Verified**: `validate_api_claims.py` checks this skill against the installed package (`statspai skill validate`). Every `sp.*` name resolves, every `sp.*(...)` call in a code block binds to the real signature (for a function that takes `**kwargs`, to its agent schema or forwarding target), and the result attributes on the gate's smoke-fit list exist. There is no version stamp to go stale. - **Paper**: Wang & Rozelle (2026), *Journal of Open Source Software* 11(125), 10604, <https://doi.org/10.21105/joss.10604>. ## Operating loop (every request) 1. **Route the mode first** from the user's words — default applied econ, Mode A epi, Mode B ML-causal, or an export-only path. Do not run the whole paper pipeline for "make Table 1" or "export this regression". A request for one estimate, one check or one table takes the short path in `references/quick-path.md`. 2. **Freeze the contract before estimating**: `y`, treatment / exposure, unit / time ids, estimand, design, required artifacts, install extras. Infer a missing field only when the column names make it obvious; otherwise return a short blocking checklist instead of inventing columns. 3. **Pick the estimator with the tables, not from memory**: `sp.route('did', design='staggered', timing_random='no', covariates='none')` (also `iv`, `rd`, `matching`, `ml_causal`, `qte`, `dynamic_panel`) returns the registered call, an example, why, and the assumptions it adds; `sp.decision_guide(family)` lists the questions. `sp.recommend(df, ...)` routes from the data itself. 4. **Start from the smallest verified call shape**: the skeleton in `references/verified-skeleton.md`, then the section snippet. For anything unfamiliar, `sp.describe_function(name)` / `sp.function_schema(name)` before writing code; `sp.search_functions("weak instrument robust CI")` to find the name. 5. **Widen one block at a time**: data contract → plan → diagnostic figure → main estimate → robustness / export. After each block read the result's `violations()`, `next_steps()`, `degradations` and `model_info` before passing it downstream. 6. **Gate the answer on artifacts**: the final message lists the files produced, the identifying assumption, the estimator class, and any failed or skipped gate. Never say "paper-ready" when an export or a required diagnostic did not actually run. 7. **Turn failures into bounded corrections**: a `StatsPAIError` carries `code`, `recovery_hint`, `diagnostics` and `alternative_functions`; fix the smallest wrong rule and continue from the last verified artifact. ## Pipeline map (default mode) ``` Paper section Step StatsPAI moves ─────────────────────────── ───── ──────────────────────────────────────────── Pre-analysis plan −1 sp.power(...) + freeze the IdentificationPlan §1 Data 0 data contract + sample-construction log §1.1 Table 1 1 sp.sumstats · sp.mean_comparison · sp.balance_table §2 Empirical strategy 2 equation + assumption + sp.causal_question(...).identify() (LLM-DAG addendum) 2.5 sp.llm_dag_propose · validate · constrained §3 Identification graphics 3 event study · first-stage F · McCrary · love plot §4 Main results (Table 2) 4 progressive controls + FE (sp.regtable / sp.causal) §5 Heterogeneity (Table 3) 5 sp.subgroup_analysis · sp.continuous_did · CATE §6 Mechanisms 6 sp.mediation · sp.decompose §7 Robustness gauntlet 7 placebo · Oster · honest_did · E-value · Conley · spec_curve §8 Replication package 8 sp.collect / sp.paper_tables · reproducibility stamp ``` Step-by-step code: `references/pipeline-econ.md`. Mode A: `references/pipeline-epi.md`. Mode B: `references/pipeline-ml-causal.md`. ## Hard rules (read before writing code) - **Estimand first.** `sp.causal_question(...).identify()` (or `sp.route`) decides DID vs RD vs IV before any estimate, and the identifying assumption is written down where a referee will read it. - **`y ~ x | fe` goes through `sp.feols`** (pyfixest). `sp.regress` is OLS only and does not parse `|`; `sp.ivreg` takes no `| fe` either. - **Staggered DID**: never a static TWFE coefficient. `sp.callaway_santanna` (+ `sp.aggte`) or `sp.sun_abraham`; event-study figures come from those results. A passed pre-trend test is not evidence for parallel trends — report `sp.honest_did` and `sp.pretrends_power`. - **Event-study paths get simultaneous bands**: `sp.uniform_bands` (sup-t) on top of `sp.event_study_vcov`; pointwise CIs do not support a claim about the whole path. - **One or a few treated units**: cluster-robust SEs over-reject; use `sp.did_few_treated` (or `sp.wild_cluster_bootstrap`). - **Treatment that feeds back into later states**: `sp.dynamic_dml`, not `sp.dml` / `sp.dml_panel`. - **Panel forests (`fe=`)**: test heterogeneity with `sp.rate_split`, not `sp.rate` on the forest's own ranking; `sp.forest_policy_tree` is fe-only. - **Covariates do not go in a TWFE / 3WFE regression** under conditional parallel trends: `sp.drdid`, `sp.callaway_santanna(..., x=[...], estimator='dr')`, `sp.ddd(..., method='dr')`. - **IV**: report the first-stage / effective F before the 2SLS coefficient (`sp.iv_diag` gives the whole bundle); below 10, `sp.anderson_rubin_ci` — not a 2SLS t-ratio. - **Parity claims are per configuration**: quote `sp.validation_scope(result)` instead of a blanket "matches Stata / R". - **RD**: `sp.rddensity` and `sp.rdplot` before the effect table; bandwidth sensitivity in robustness. - **Matching / weighting**: balance (`sp.love_plot`, `sp.ps_balance`) before outcomes; say which estimand (ATT / ATE / ATO). - **Export all three formats** (`.to_word`, `.to_excel`, `.to_latex`) from the same `RegtableResult` / `Collection`; never hand-roll LaTeX. - **Citations only from `result.cite()` / `sp.bibtex(keys=[...])`** — never from memory (paper.bib is the single source of truth). - **Plots return `(fig, ax)`**: unpack, then `fig.savefig(...)`. ## Where to read next | Need | File | | --- | --- | | **one estimate, one check or one export**: route, describe, fit, inspect, export, and when to stop | `references/quick-path.md` | | operating loop in full, acceptance gates by request type, mode table, figure & table inventory | `references/operating-loop.md` | | the minimal end-to-end skeleton to copy | `references/verified-skeleton.md` | | Steps −1 → 8 with code, one paper section each | `references/pipeline-econ.md` | | target-trial / IPTW / g-formula / TMLE / MR / survival pipeline | `references/pipeline-epi.md` | | DML / meta-learners / causal forest / policy / conformal / fairness pipeline | `references/pipeline-ml-causal.md` | | Word / Excel / LaTeX export, regtable recipes, the 12 standard figures, notebook setup | `references/export.md` | | method catalog by family; when to use StatsPAI vs alternatives | `references/method-catalog.md` | | robust staggered-DID family, event-study bands, few treated units, IV bundle, GRF forest family, dynamic DML, validation scope | `references/modern-methods.md` | | API traps that cost agents real time; agent integration pattern | `references/common-mistakes.md` | | driving StatsPAI over MCP (`statspai-mcp`) or the shell (`statspai run`), data handles, routing, the result contract | `references/mcp-and-cli.md` | ## Validation `statspai skill validate` (or `python validate_api_claims.py`) runs the gate: every `sp.<name>` in this skill exists, the documented argument names bind, and the documented result attributes hold on a live fit. `EVALS.md`-style held-out checks for editing the skill itself live in the repository under `StatsPAI_full_data_analysis_skill/EVALS.md`.