18-EFT.WP.Methods.CrossStats v1.0 | Appendix C — Manifest Templates & Examples (stats manifest)

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Appendix C — Manifest Templates & Examples (stats manifest)

One-line objective: Provide the minimal key set, field semantics, validation rules, and multi-scenario examples for the cross-statistics publication manifest manifest.stats, ensuring estimates, tests, drift monitors, experiments, and audits are reproducible, traceable, and alignable across systems.

I. Scope & Targets

• This appendix defines the persisted structure of statistical deliverables, applicable to offline evaluation, online experimentation, drift monitoring, causal estimation, and calibration transfer.
• Inputs: metrics, intervals, and diagnostics produced throughout this volume; cross-volume mandatory metadata (see Methods.Cleaning v1.0 and Methods.Imaging v1.0).
• Output: a single JSON document manifest.stats containing TraceID, versioning, window Delta_t, time-base mappings, contract evaluations, and signature.

II. Minimal Key Set (must exist)

• schema_version, book_ref, release_tag
• TraceID, repro_hash, signature
• timebase.tau_mono_range, timebase.ts_range, timebase.offset/skew/J
• arrival.two_forms.delta_form, arrival.two_forms.tol_Tarr
• window.Delta_t, dataset.N, weights.W_norm
• metrics.core[*] (name, estimate, interval or posterior quantiles, units)
• contracts[*] (id, status, severity, evidence)
• actions[*] (strategy-card decisions and dispositions)
• provenance (data/code origins and environment summary)

III. Field & Type Specifications

1. schema_version : string (e.g., "1.0.0")
2. book_ref : string (fixed: "EFT.WP.Methods.CrossStats v1.0")
3. TraceID : string (cross-system trace ID)
4. repro_hash : string (hash_sha256(code+params+data_fingerprint))
5. signature : string (publisher’s signature)
6. timebase : object
   • tau_mono_range : [int,int] (internal monotone time-base range)
   • ts_range : [string,string] (ISO8601 external publication range)
   • offset/skew/J : {offset: double, skew: double, J: double}
7. arrival.two_forms : {delta_form: double, tol_Tarr: double}
8. window.Delta_t : string (e.g., "PT24H")
9. dataset : {N: int, N_eff?: double, sampling?: string}
10. weights : {W_norm: double, cap_w?: double, p_trim?: double}
11. metrics.core[*] : {name: string, est: double, se?: double, ci?: [double,double], posterior?: {q05: double, q50: double, q95: double}, unit?: string, dim?: string, notes?: string}
12. metrics.drift? : {W1?: double, KL?: double, psi?: double}
13. metrics.ab? : {lift: double, se: double, ci: [double,double], mde?: double, alpha_spent?: double}
14. metrics.causal? : {ATE: double, U?: double, SMD_max?: double, overlap_min?: double}
15. contracts[*] : {id: string, status: string, severity: string, evidence: object}
16. actions[*] : {policy_id: string, decision: string, reason: string, at: string}
17. provenance : {data_uri: string, code_uri: string, env: {python?: string, pkg?: object}}

IV. Template: Minimal Publishable Manifest

{

"schema_version": "1.0.0",

"book_ref": "EFT.WP.Methods.CrossStats v1.0",

"release_tag": "stats-prod-2025-08-31T12:00Z",

"TraceID": "trc_01HXYZ...",

"repro_hash": "sha256:REPRO_HASH",

"signature": "SIG_BASE64",

"timebase": {

"tau_mono_range": [1725062400, 1725148800],

"ts_range": ["2025-08-31T00:00:00Z", "2025-09-01T00:00:00Z"],

"offset/skew/J": {"offset": 0.0012, "skew": 2.3e-6, "J": 0.0031}

},

"arrival": {

"two_forms": {

"delta_form": 2.1e-6,

"tol_Tarr": 5.0e-6

}

},

"window": {"Delta_t": "PT24H"},

"dataset": {"N": 125034},

"weights": {"W_norm": 0.9996},

"metrics": {

"core": [

{"name": "conversion_rate", "est": 0.0842, "ci": [0.0831, 0.0853], "unit": "1", "dim": "[]"},

{"name": "avg_order_value", "est": 56.73, "se": 0.42, "unit": "USD", "dim": "[M]"}

]

},

"contracts": [

{"id": "C30-000", "status": "pass", "severity": "info", "evidence": {"checks": 128}},

{"id": "C30-001", "status": "pass", "severity": "info"},

{"id": "C30-004", "status": "pass", "severity": "info", "evidence": {"W_norm": 0.9996}},

{"id": "C30-342", "status": "pass", "severity": "info", "evidence": {"coverage_rate": 0.949}}

],

"actions": [

{"policy_id": "SC-SLO-01", "decision": "ship", "reason": "all guardrails pass", "at": "2025-08-31T12:01:05Z"}

],

"provenance": {

"data_uri": "s3://bucket/ds/2025-08-31/",

"code_uri": "git+https://repo/commit/abcdef",

"env": {"python": "3.11.5", "pkg": {"numpy": "2.0.1", "scipy": "1.14.0"}}

}

}

V. Example A: Online A/B Experiment (sequential alpha, guardrail SLOs)

{

"schema_version": "1.0.0",

"book_ref": "EFT.WP.Methods.CrossStats v1.0",

"release_tag": "ab-exp-42-int-07",

"TraceID": "trc_AB42_07",

"repro_hash": "sha256:HASH_AB42_07",

"signature": "SIG_BASE64",

"timebase": {

"tau_mono_range": [1725148800, 1725235200],

"ts_range": ["2025-09-01T00:00:00Z", "2025-09-02T00:00:00Z"],

"offset/skew/J": {"offset": 0.0007, "skew": 2.0e-6, "J": 0.0025}

},

"arrival": { "two_forms": {"delta_form": 1.8e-6, "tol_Tarr": 5.0e-6} },

"window": {"Delta_t": "PT24H"},

"dataset": {"N": 98023, "sampling": "online_randomized"},

"weights": {"W_norm": 1.0002},

"metrics": {

"core": [

{"name": "lift_cr_B_vs_A", "est": 0.0124, "se": 0.0039, "ci": [0.0048, 0.0200], "unit": "1", "dim": "[]"},

{"name": "guardrail_latency_ms_p99", "est": 245.0, "unit": "ms", "dim": "[T]"}

],

"ab": {

"lift": 0.0124,

"se": 0.0039,

"ci": [0.0048, 0.0200],

"mde": 0.01,

"alpha_spent": 0.043

}

},

"contracts": [

{"id": "C30-382", "status": "pass", "severity": "info", "evidence": {"latency_ms_p99": 245}},

{"id": "C30-383", "status": "pass", "severity": "info", "evidence": {"alpha_spent": 0.043, "alpha_budget": 0.05}},

{"id": "C30-381", "status": "pass", "severity": "info", "evidence": {"p_t": 0.501, "p_c": 0.499, "eps_exp": 0.01}}

],

"actions": [

{"policy_id": "SC-AB-01", "decision": "ship", "reason": "sequential boundary crossed; guardrails pass", "at": "2025-09-02T00:00:30Z"}

],

"provenance": {

"data_uri": "kafka://topic/exp42/day=2025-09-01",

"code_uri": "git+https://repo/commit/1122aabb",

"env": {"python": "3.11.5", "pkg": {"pandas": "2.2.2", "statsmodels": "0.14.2"}}

}

}

VI. Example B: Distribution Drift Monitoring (alignment & recalibration trigger)

{

"schema_version": "1.0.0",

"book_ref": "EFT.WP.Methods.CrossStats v1.0",

"release_tag": "drift-week-2025W36",

"TraceID": "trc_DRIFT_W36",

"repro_hash": "sha256:HASH_DRIFT_W36",

"signature": "SIG_BASE64",

"timebase": {

"tau_mono_range": [1725148800, 1725753600],

"ts_range": ["2025-09-01T00:00:00Z", "2025-09-08T00:00:00Z"],

"offset/skew/J": {"offset": 0.0011, "skew": 2.6e-6, "J": 0.0030}

},

"arrival": { "two_forms": {"delta_form": 2.5e-6, "tol_Tarr": 5.0e-6} },

"window": {"Delta_t": "P7D"},

"dataset": {"N": 705_211},

"weights": {"W_norm": 1.0000},

"metrics": {

"core": [

{"name": "score_calibration_ece", "est": 0.024, "unit": "1", "dim": "[]"}

],

"drift": {"W1": 0.095, "KL": 0.021, "psi": 0.14}

},

"contracts": [

{"id": "C30-370", "status": "fail", "severity": "warn", "evidence": {"W1": 0.095, "W1_max": 0.08}},

{"id": "C30-373", "status": "fail", "severity": "error", "evidence": {"r_win": 3}}

],

"actions": [

{"policy_id": "SC-DRIFT-01", "decision": "align_then_recalibrate", "reason": "persistent W1 breach; psi elevated", "at": "2025-09-08T00:01:00Z"},

{"policy_id": "SC-CAL-01", "decision": "canary_10pct", "reason": "ECE_after improves >= delta_min", "at": "2025-09-09T12:00:00Z"}

],

"provenance": {

"data_uri": "s3://bucket/weekly_snap/2025W36",

"code_uri": "git+https://repo/commit/55cc66dd",

"env": {"python": "3.11.5", "pkg": {"scikit-learn": "1.5.1"}}

}

}

VII. Example C: Causal Estimation (doubly-robust with overlap checks)

{

"schema_version": "1.0.0",

"book_ref": "EFT.WP.Methods.CrossStats v1.0",

"release_tag": "causal-ATE-geoQ3",

"TraceID": "trc_CAUSAL_GEO_Q3",

"repro_hash": "sha256:HASH_CAUSAL_GEO_Q3",

"signature": "SIG_BASE64",

"timebase": {

"tau_mono_range": [1719782400, 1727568000],

"ts_range": ["2024-07-01T00:00:00Z", "2024-09-30T23:59:59Z"],

"offset/skew/J": {"offset": 0.0009, "skew": 1.8e-6, "J": 0.0021}

},

"arrival": { "two_forms": {"delta_form": 1.2e-6, "tol_Tarr": 5.0e-6} },

"window": {"Delta_t": "P92D"},

"dataset": {"N": 40211, "N_eff": 27894.5, "sampling": "observational"},

"weights": {"W_norm": 1.0007, "cap_w": 20.0, "p_trim": 0.7},

"metrics": {

"core": [

{"name": "ATE", "est": 1.84, "ci": [0.95, 2.73], "unit": "USD", "dim": "[M]"}

],

"causal": {"ATE": 1.84, "U": 0.62, "SMD_max": 0.06, "overlap_min": 0.04}

},

"contracts": [

{"id": "C30-400", "status": "pass", "severity": "info", "evidence": {"overlap_min": 0.04, "eps_ol": 0.02}},

{"id": "C30-401", "status": "pass", "severity": "info", "evidence": {"SMD_max": 0.06, "smd_max": 0.10}},

{"id": "C30-402", "status": "pass", "severity": "info", "evidence": {"ATE_IPW": 1.79, "ATE_OR": 1.88}},

{"id": "C30-350", "status": "pass", "severity": "info", "evidence": {"B": 2000}}

],

"actions": [

{"policy_id": "SC-COVER-01", "decision": "publish_readonly", "reason": "coverage met; trimmed weights applied", "at": "2024-10-01T10:00:00Z"}

],

"provenance": {

"data_uri": "warehouse://table/geo_q3",

"code_uri": "git+https://repo/commit/aa77bb88",

"env": {"python": "3.10.14", "pkg": {"econml": "0.15.0", "pymc": "5.13.1"}}

}

}

VIII. Validation & Assertions (automated)

• Structure checks
  required_keys ⊆ manifest.stats.keys(); required fields in arrays must not be omitted.
• Dimensional consistency
  For metrics.core[*], verify unit(x) and dim(x) consistency: check_dim( est - ref_unit_transform(est) ) = true.
• Time-base & arrival-time
  non_decreasing(tau_mono_range); delta_form ≤ tol_Tarr.
• Weights & samples
  |W_norm - 1| ≤ tol_w; if N_eff is provided, validate N_eff = ( ( ∑ w )^2 ) / ( ∑ w^2 ).
• Contracts & strategies
  contracts[*].id belongs to the C30-* namespace; status ∈ {pass, fail}; severity ∈ {info, warn, error, fatal}; actions[*].policy_id belongs to the strategy-card namespace (e.g., SC-DRIFT-01).

IX. Mapping to the Contract Library

• Each contracts[*] item maps to a C30-* entry from Appendix B; evidence carries the triggering metrics (e.g., coverage_rate, alpha_spent, W1/KL/psi, SMD_max).
• Each actions[*] item maps to an Appendix B strategy card SC-*, recording decision, rationale, and timestamp.
• Failure items must align with the release gates in Methods.Cleaning v1.0, Chapter 10: assert_contract(ds, tests) -> report.

X. Traceability & Signature Specification

• repro_hash = hash_sha256( code_uri ∥ params ∥ data_fingerprint )
• signature = sign( private_key, repro_hash ); verify with verify(public_key, signature, repro_hash) = true
• Record provenance.env so containers and package versions are reconstructible.

XI. Versioning & Compatibility

• schema_version follows MAJOR.MINOR.PATCH.
  MAJOR changes may break readers; MINOR adds optional fields; PATCH refines descriptive notes.
• Backward-compatibility policy
  Readers should backfill defaults for newly added optional fields (e.g., optional subkeys under metrics.*) across MINOR increments.

Summary

• This template consolidates core metrics, dual T_arr formulations, time-base semantics, and the contract–strategy loop into a unified persisted manifest.stats.
• With the minimal key set and worked examples (A/B, drift, causal), it enables cross-system reuse of statistical results, compliance auditing, and rollback-ready publication.