sdid
Estimates treatment effects from panel data using synthetic difference-in-differences, validating parallel trends via control weighting and producing placebo/bootstrap confidence intervals.
Instructions
Synthetic Difference-in-Differences estimator (and SC / DID variants). Validation: certified parity evidence. Do NOT use when: there is no clean pre-treatment block for every unit -- the unit and time weights are fit on the pre-period grid. Cost: Placebo / bootstrap standard errors refit the full weighting problem n_reps times; the point estimate alone is cheap. Lower n_reps while iterating. Assumptions: Parallel trends in the absence of treatment, after the synthetic/DiD weighting; No anticipation and no interference between units (SUTVA); The control pool's outcome process is stable around the intervention. Pre-conditions: Panel with treated and control units and a clear treatment date; Pre-treatment periods available to assess comparability of trends. Failure modes: Weighted pre-treatment trends still diverge between treated and synthetic control -> Inspect the unit/time weights and pre-trend fit; consider event-study DiD with honest bounds. Alternatives: sp.synth, sp.augsynth, sp.callaway_santanna, sp.gardner_did. Typical minimum N: 15.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| y | No | Outcome variable column name or outcome array. | |
| seed | No | Random seed for reproducibility. | |
| time | No | Time period column. | |
| unit | No | Unit identifier column. | |
| alpha | No | Significance level for confidence intervals. | |
| detail | No | Payload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip. | agent |
| method | No | * ``'sdid'`` -- Synthetic DID (unit + time weights) * ``'sc'`` -- Synthetic Control (unit weights only) * ``'did'`` -- DID (uniform weights) | sdid |
| n_reps | No | Replications for placebo / bootstrap SE. | |
| backend | No | ``'native'`` uses StatsPAI's Python implementation. ``'synthdid'``/``'r'`` delegates to the R ``synthdid`` package through ``Rscript`` and returns the reference package's point estimate and ``synthdid_se`` standard error. The R backend is mainly for exact cross-language parity claims; the dependency- light native implementation remains the default. | native |
| outcome | No | Outcome variable column. Alias ``y=`` accepted for R-style calls. | |
| as_handle | No | If true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running. | |
| data_path | Yes | Absolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://. | |
| result_id | No | Optional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns. | |
| se_method | No | Standard-error method (see Notes). | placebo |
| covariates | No | Reserved for future covariate-adjusted extensions. | |
| treat_time | No | treat_time parameter. | |
| treat_unit | No | treat_unit parameter. | |
| data_columns | No | Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads. | |
| treated_unit | No | Treated unit(s). Alias ``treat_unit=`` accepted. | |
| data_sample_n | No | Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels. | |
| treatment_time | No | First treatment period (inclusive). Alias ``treat_time=`` accepted. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||