qte_hd_panel
Estimate quantile treatment effects in panel data with high-dimensional controls, using double-selection LASSO to select covariates and bootstrap inference for valid confidence intervals.
Instructions
Panel quantile treatment effects with high-dimensional controls. Validation: validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact). Assumptions: Selection-on-observables (unconfoundedness + overlap) or, for IV variants, instrument validity; For IV-QTE: rank invariance / rank similarity (monotonicity of the structural quantile function). Pre-conditions: Covariates, treatment, and outcome; for IV-quantile methods, a valid instrument; Enough data to estimate the outcome distribution across quantiles. Failure modes: Estimated conditional quantiles cross (non-monotone), or tail quantiles are unstable -> Use rearrangement / monotonization and avoid extreme quantiles where data are sparse. Alternatives: sp.qte, sp.iv, sp.dml. Typical minimum N: 500.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| y | Yes | Outcome variable column name or outcome array. | |
| se | No | ``'bootstrap'`` resamples **units**, preserving within-unit dependence. There is no analytic option: Canay's two-step variance depends on the first step, and a naive quantile-regression SE would understate it. | bootstrap |
| seed | No | Random seed for reproducible stochastic steps. | |
| time | Yes | Time period column. | |
| unit | Yes | Unit identifier column. | |
| alpha | No | Significance level for confidence intervals and tests. | |
| treat | Yes | Treatment indicator or first-treatment-period column. | |
| 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 | See the module docstring. ``'canay'`` assumes the individual effect is a pure location shift and needs a reasonably long panel. | canay |
| n_boot | No | Number of bootstrap replications. | |
| 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://. | |
| quantiles | No | Defaults to ``(0.1, 0.25, 0.5, 0.75, 0.9)``. | |
| 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. | |
| covariates | Yes | Candidate control set; narrowed by double-selection LASSO. | |
| lasso_alpha | No | Penalty on standardised covariates. ``None`` uses the Belloni-Chernozhukov-Hansen plug-in penalty. | |
| data_columns | No | Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads. | |
| data_sample_n | No | Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||