QRF amount forests lose donor design weights after the n-of-n bootstrap: extreme-tail draws are a seed lottery

Summary

The QRF's design-weight handling for amount forests is a single n-of-n weighted bootstrap of the positive donors, after which the forest fits unweighted and within-leaf quantiles are occurrence-weighted over bootstrap multiplicities (qrf.py:187-216, :493-513; the module header documents the design). For heavy-tailed sparse dollar targets this amplifies the extreme tail by construction:

• a tiny-weight extreme donor's inclusion is a coin flip (the non_sch_d_capital_gains $594,483.64 row: 0.00755% of positive donor weight, expected bootstrap copies 0.47, present-at-least-once probability 37.5%);
• once included it is floored at 1/n of the training array (2.13× its weight share for that row), and empirically drawn at 7.11× its weight share on Build M (4 of 7,452 positive draws);
• the larger donors ($5.96M actual max, weight 1.1) were all omitted at seed 0 — so the shipped tail is a bootstrap lottery, not a bounded estimate, and a different seed could ship a far worse ceiling.

Deterministic replay evidence (seed-0 production target sequence reproduced the exact bootstrap composition and the $594,483.6449131259 ceiling) is in the #462 fix-3b diagnostic record.

Ask

Weight-aware within-leaf draws (or an equivalent estimator change that keeps original design weights available at draw time), so a donor's draw probability tracks its weight share instead of its bootstrap multiplicity. This is the root statistical fix behind #462's interim per-target tail bound and should come with cross-validation evidence on the affected stages.

Blast radius (from the diagnostic)

QRF is the public alias — every consumer redraws: ~25 US runtime constructors (puf_tax_detail chain, SCF, SIPP, ORG, housing, retirement, disability, voluntary filing, …) plus two UK SPI stages. Behavioral tests to adapt are enumerated in the diagnostic (weighted-fit contract, tail-share/interpolation, RNG determinism, chain bit-equality — behavioral constraints, not goldens).

Relates #462 (interim bound), #457 (same failure family in the frozen eCPS), #464 (tail-concentration gate = the backstop that detects this class).

Start in qrf.py:187-216 and 493-513, then read the #462 fix-3b diagnostic for the bootstrap composition and affected behavioral constraints. Trace the QRF public alias consumers and run the weighted-fit, tail-share/interpolation, RNG determinism, and chain bit-equality tests. Done means donor draw probabilities retain original design weights and cross-validation covers the affected stages without relying on seed-specific tail behavior.