Reuse one TSL SurveyDesign (with FPC) across brand-awareness phases (P1)

CI re-review P1: `bench_brand_awareness_survey.py` declared the
analytical TSL path with `SurveyDesign(weights, strata, psu, fpc, nest)`
only in phase 2; phases 4 (multi-outcome), 6 (placebo), and 7 (event
study + HonestDiD) built their own SurveyDesigns without `fpc`. That
means a material share of the committed brand-awareness baselines
timed a different variance path than the scenario doc declares.

Fix:
- One analytical `sd_tsl` SurveyDesign (strata + PSU + FPC + nest=True)
  is now constructed once at the top of `make_phases` and reused across
  phases 2, 4, 6, and 7. Phase 3 (replicate weights, JK1) is a
  different variance surface and correctly keeps its own design.
- Regenerated baselines for both backends.
- Regenerated findings tables via gen_findings_tables.py.

Narrative refreshed against the new tables:

- Brand-aware medium: on Python JK1 now leads by ~2.2x (was 1.9x in
  the previous rerun); on Rust the multi-outcome loop and JK1 come in
  essentially tied. Medium is also where Python is slowest relative
  to Rust (~1.6x) - the full analytical TSL path with FPC exposes
  vectorization differences at that shape. Totals re-converge at
  large scale.
- Reversible dCDH: ~48-52% split under both backends (previously the
  Python heterogeneity refit edged out the main fit slightly).
- Scaling finding #5 retuned: Rust-only uplift is still the SDiD
  story; brand-aware medium now surfaces as a secondary, modest
  ~1.6x case rather than "within noise."

Still measurement only. No changes under diff_diff/ or rust/.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

benchmarks/speed_review/baselines/brand_awareness_survey_large_python.json

@@ -2,47 +2,47 @@
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-    "peak_mb": 335.36,
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     "sampler_interval_s": 0.01
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     "5_check_parallel_trends": {
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     "6_placebo_refit_pre_period": {
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benchmarks/speed_review/baselines/brand_awareness_survey_large_rust.json

@@ -2,47 +2,47 @@
   "scenario": "brand_awareness_survey_large",
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     "5_check_parallel_trends": {
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     "7_event_study_plus_honest_did": {
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benchmarks/speed_review/baselines/brand_awareness_survey_medium_python.json

@@ -2,47 +2,47 @@
   "scenario": "brand_awareness_survey_medium",
   "backend": "python",
   "has_rust_backend": false,
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   "memory": {
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-    "start_mb": 134.75,
-    "peak_mb": 182.52,
-    "growth_mb": 47.77,
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+    "peak_mb": 186.22,
+    "growth_mb": 52.36,
     "sampler_interval_s": 0.01
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   "phases": {
     "1_naive_fit_no_survey_design": {
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     "2_tsl_strata_psu_fpc": {
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     "3_replicate_weights_jk1": {
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     "5_check_parallel_trends": {
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benchmarks/speed_review/baselines/brand_awareness_survey_medium_rust.json

@@ -2,47 +2,47 @@
   "scenario": "brand_awareness_survey_medium",
   "backend": "rust",
   "has_rust_backend": true,
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     "sampler_interval_s": 0.01
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   "phases": {
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     "5_check_parallel_trends": {
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     "7_event_study_plus_honest_did": {
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benchmarks/speed_review/baselines/brand_awareness_survey_small_python.json

@@ -2,47 +2,47 @@
   "scenario": "brand_awareness_survey_small",
   "backend": "python",
   "has_rust_backend": false,
-  "total_seconds": 0.21071716699999998,
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   "memory": {
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-    "growth_mb": 11.81,
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+    "peak_mb": 127.31,
+    "growth_mb": 12.0,
     "sampler_interval_s": 0.01
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benchmarks/speed_review/baselines/brand_awareness_survey_small_rust.json

@@ -2,47 +2,47 @@
   "scenario": "brand_awareness_survey_small",
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   "has_rust_backend": true,
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     "sampler_interval_s": 0.01
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