[soodoku]

DoubleML currently asks users to choose a fixed repetition count. This proposal adds a scalar mean diagnostic over existing per-repetition coefficients and an opt-in sequential driver for a single-treatment, nonclustered PLR model.

The driver fits a private one-repetition model on independently shuffled partitions, checks after burn-in, never refits earlier repetitions, and returns either target_reached or budget_exhausted. Its separate mean result carries no sampling SE or p-value; the supplied model and its median-based inference are preserved. Fold-specific tuning, custom scores, clustered data, and multiple treatments are explicitly excluded.

The normal stopping rule follows Ritzwoller–Romano. Its error-rate interpretation is asymptotic. In 1,000 independent pairs on fixed synthetic data, tolerance 0.02 used 29.953 repetitions on average and had 6.0% exceedance (95% Monte Carlo interval 4.69%–7.65%); tolerance 0.01 used 122.261 repetitions and had 6.2% exceedance (4.87%–7.87%). Neither exhausted the budget. These results do not establish exact 5% calibration.

Validation completed:

• 39 feature tests; 100% statement coverage of the new module.
• 774 utility/PLR/default regression tests.
• Ruff, pinned Black across project source, configured mypy, and documentation lint.
• Source distribution and wheel build; new exports verified in the wheel.
• API documentation builds; 21 warnings concern existing dataset references and inaccessible online inventories.

Full pytest run: 11,588 passed, 66 skipped, 2 failed. Both failures are existing LPLR Optuna tuning assertions (nuisance_space-ml_a0-random and nuisance_space-ml_a0-tpe, line 93 of test_lplr_tune_ml_models.py). Both also fail in a clean snapshot of upstream 469daf260d4ea3520b8265acf17a9d29e800c5e0 using the same environment; no LPLR code is changed here. The full run used four pytest workers and unchanged upstream downloads for its two remote fixtures through a temporary local test adapter. The final six additional feature cases were checked separately; all 39 feature cases pass.

This is a draft review PR in the finite-sample fork, before upstream submission. The benchmark source, raw results, and limitations are included under benchmarks/.

This pull request includes code written with the assistance of AI.
The code has not yet been reviewed by a human.

[soodoku]

Superseded by the ready-for-review upstream PR: DoubleML#429 . The feature branch is retained.