Architecture swarm · Territory-analysis + market-scoring agent · Franchise-system-performance-correlation skill · Build pillar · Published July 23, 2026

How to build per-unit per-driver EBITDA causal decomposition for franchise systems

Franchise systems comparing per-unit EBITDA across hundreds of units face a fundamental question: why does Unit 47 generate $480k of EBITDA while Unit 213 generates $220k on similar revenue? Tableau + Looker + Power BI + Domo + Sisense + Qlik + ThoughtSpot + Mode + Hex + Sigma + Omni + Lightdash + Metabase + Preset + Redash + Cube + Holistics + InsightSquared ship per- tenant flat descriptive primitives. None decomposes the gap into per-driver causal contributions. The Map + Estimate + Gate + Audit skill bundle on the territory-analysis-market-scoring agent sits above the BI surface and writes a per-unit per-driver EBITDA causal decomposition with counterfactual simulation, sensitivity analysis, and named regulatory anchors preserved in every audit record: FDD Item 19 FPR substantiation, 30-state franchise relationship laws, Sarbanes-Oxley Section 302 / 404 / 906, SEC Regulation G non-GAAP, FASB ASC 606, Robinson-Patman, replication-crisis statistical discipline, ECOA disparate-impact, EU AI Act Annex III high-risk classification, NIST AI RMF.

Start with a Tier 1 AI Readiness Assessment Tier 3 Fractional CMO with AI Swarm Take the 3-question quiz

The 4-skill bundle on the territory-analysis + market-scoring agent

Map

Catalogs the 37 standing drivers across three layers (trade- area + unit + operational). Constructs a directed-acyclic- graph (DAG) causal graph and runs Pearl do-calculus identification with back-door criterion + front-door criterion + instrumental-variable identification + mediator identification + collider identification + confounder identification. Each DAG edge carries a per-edge confidence tier and explainability trace written into Audit.

Estimate

Bayesian hierarchical multi-level model (Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro) respects unit-within-franchisee-within-region nesting. CATE estimation (T-learner + S-learner + X-learner + DR-learner via CausalML + DoubleML + EconML + Causal Forest + BART). Synthetic Control Method + augmented; DiD + triple-differences + event study + Callaway-Sant’Anna + Goodman-Bacon; RD; IV + 2SLS; Heckman correction; propensity-score matching; IPW; doubly-robust; G-computation; TMLE. Sensitivity analysis (Rosenbaum bounds + E-value + tipping-point + prior- sensitivity). Counterfactual simulation per-driver scenarios with Bayesian + Monte Carlo + bootstrap + quantile-regression prediction intervals + Borda-Condorcet rank aggregation + SHAP / LIME / PFI driver importance + intervention-cost-vs- EBITDA-uplift + ROI / IRR. LLM-augmented tie-breakers under per-vendor zero-retention.

Gate

Five anchors before delivery. FDD Item 19 FPR substantiation per FTC Franchise Rule 16 CFR 436 + NASAA Commentary + 15- state franchise registration + 7-additional-state disclosure + 30-state franchise relationship laws + FDD Item 12 + 17. Sarbanes-Oxley 302 / 404 / 906 + PCAOB AS 2201 + SEC Regulation G + SEC C&DI Q100 / 101 / 102 + SEC Reg S-K Item 303 + FASB ASC 606 / 842 / 326 + AICPA non-GAAP + PCAOB AS 2410. Robinson-Patman + Lanham + per-state UDAP + state business-opportunity + state investment disclosure + CFPB UDAAP. Replication-crisis statistical discipline (Rosenbaum + E-value + tipping-point + prior-sensitivity + multiple- comparisons correction Bonferroni + Holm + Benjamini-Hochberg FDR + Tukey HSD + Dunnett + Scheffe + pre-registration AsPredicted + OSF). ECOA Regulation B + Fair Housing + GDPR Article 22 + CCPA opt-out + EU AI Act Article 22 + 26 + 50 + Annex III + Article 9 + 13 + 14 + 15 + NIST AI RMF + ISO 42001 + per-vendor LLM zero-retention. Policy-as-code via OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io.

Audit

Per-unit per-driver WORM record at every decomposition run + every counterfactual simulation. Storage: AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi WORM. Retention stacks (longest applicable wins): 7- year SOX Section 802 + 7-year IRS + 7-year FTC + 7-year FDD + per-state franchise + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8. End-to- end replay rewinds every stage with confidence tier and explainability. FBC feedback loop captures realized vs counterfactual delta and recalibrates the DAG + Bayesian priors + CATE + SCM / DiD / RD / IV + propensity scores + doubly-robust / TMLE + sensitivity analysis.

The real vendor ecosystem this sits above

BI dashboards + numerical stack

Tableau + Looker + Power BI + Domo + Sisense + Qlik + ThoughtSpot + Mode + Hex + Sigma + Omni + Lightdash + Metabase + Preset + Redash + Cube + Holistics + InsightSquared BI dashboards remain the descriptive substrate where the decomposition’s outputs land for franchise- ops review. scikit-learn + PyTorch + JAX + statsmodels + tslearn numerical stack backs the causal-inference stack.

Bayesian + causal-inference + experiment tracking

Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro Bayesian probabilistic programming. CausalML + DoubleML + EconML + Causal Forest + BART causal-inference libraries. SHAP + LIME + Captum + DALEX + InterpretML explainability. MLflow + Weights & Biases + CometML + Neptune.ai + ClearML experiment tracking. OpenAI + Anthropic LLM tie- breakers under per-vendor zero-retention. LangSmith + Arize + WhyLabs + Helicone + Langfuse + PromptLayer + Galileo observability.

Disclosure-management + policy-as-code + WORM

Workiva + BlackLine + FloQast + Trintech + AuditBoard + Hyperproof + Drata + Vanta disclosure-management remain where final XBRL tagging happens. OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io policy-as- code expresses the FDD Item 19 + SOX + Reg G + Robinson- Patman + replication-crisis + EU AI Act gates. AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi compliance WORM holds the per-unit per-driver audit substrate.

The 6-workstream reporting cycle

Numeric uplift commitments are not made up-front. The engagement ships a pre-engagement baseline across six workstreams; the cycle tracks delta against that baseline. Reporting is the substrate, not the promise.

Map coverage. Per-driver coverage across the 37 standing drivers across trade-area + unit + operational layers; DAG construction completeness; Pearl do-calculus identification coverage; per-edge confidence-tier distribution.
Estimate quality. Bayesian hierarchical posterior diagnostic (R-hat + effective sample size + divergent transitions); CATE method agreement; SCM + DiD + RD + IV method agreement; counterfactual confidence-interval coverage (Bayesian + Monte Carlo + bootstrap + quantile regression); sensitivity-analysis (Rosenbaum + E-value + tipping-point + prior-sensitivity) completeness.
Gate quality. Per-anchor evaluation completeness (FDD Item 19 FPR substantiation + 30-state FRR + SOX 302 / 404 / 906 + Reg G non-GAAP + FASB ASC + Robinson- Patman + replication-crisis correction + ECOA + EU AI Act Annex III); per-anchor pass / fail / route-to-counsel distribution; FPR substantiation document completeness; multiple-comparisons correction adherence.
Audit quality. Per-unit per-driver WORM record completeness; retention-window coverage (longest of 7- year SOX 802 + 7-year IRS + 7-year FTC + 7-year FDD + per- state franchise + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8); end-to-end replay success rate.
Compliance posture. FDD Item 19 FPR substantiation file completeness; SOX 906 certification readiness; SEC Reg G reconciliation completeness when 4-wall EBITDA + Adjusted EBITDA + same-store-sales + comp-sales + cohort-LTV reach public-co or PE-sponsor disclosure; Robinson- Patman exposure; ECOA disparate-impact audit cadence; EU AI Act Annex III + Article 50 disclosure coverage.
Audit-trail completeness. Per-anchor regulatory citation completeness; FBC feedback-loop recalibration cadence; sibling-handoff pointer completeness into the territory-analysis-market-scoring bundle (parent franchise-system-performance-correlation commercial + site- selection software + market-opportunity analysis + competitor mapping + franchise-territory mapping + peer-cohort benchmarking + franchisee accountability dashboard + revenue drivers analysis sibling skill on the rollup-reporting agent + benchmark report) and into per-prospect franchise-territory analysis pipeline + continuous per-market scoring + FDD territorial-protection gating + per-franchisee accountability views + cohort-framed benchmark reports + marketing forecasting + per-location MMM + root-cause attribution sketch + quarterly board deck + PE-sponsor LP letter drafting.

Frequently asked questions

What is per-unit per-driver EBITDA causal decomposition for franchise systems — and why is Tableau + Looker descriptive output not enough?

Franchise systems comparing per-unit EBITDA performance across hundreds of units face a fundamental question: why does Unit 47 in Tampa generate $480k of EBITDA while Unit 213 in Austin generates $220k on similar revenue? Tableau, Looker, Power BI, Domo, Sisense, Qlik, ThoughtSpot, Mode, Hex, Sigma, Omni, Lightdash, Metabase, Preset, Redash, Cube, Holistics, and InsightSquared ship per-tenant flat descriptive primitives — totals, averages, cohort comparisons, dashboards. None decomposes the EBITDA gap into per-driver causal contributions across the standing 37+ driver set (trade-area demographics + foot traffic + real-estate cost + labor availability + competitor density + co-tenancy + weather + seasonality + cross-banner cannibalization + unit tenure + multi-unit-operator status + franchisee experience + franchisee engagement + staff tenure + staff turnover + manager tenure + square footage + format + equipment vintage + remodel recency + loyalty penetration + AOV + conversion rate + channel mix + marketing-mix coefficient + pricing strategy + promotion cadence + product-mix margin + supply cost + labor cost + occupancy cost + royalty rate + marketing-fund contribution + FDD Item 19 FPR bucket). The four-skill bundle on the territory-analysis-market-scoring agent — Map, Estimate, Gate, Audit — sits above the BI surface and writes a per-unit per-driver EBITDA causal decomposition with counterfactual simulation, sensitivity analysis, confidence intervals, and named regulatory citations preserved in the audit trail.

Why do Tableau + Looker + Power BI + Domo + Sisense + Qlik + ThoughtSpot break at multi-franchise causal-decomposition scale?

Each BI vendor ships a per-tenant flat-dashboard primitive — descriptive statistics + cohort comparison + visualization. None constructs a directed-acyclic-graph (DAG) causal graph under Pearl do-calculus + back-door + front-door + instrumental-variable identification. None runs a Bayesian hierarchical multi-level model that respects the per-unit nesting (units within franchisees within regions within banners). None computes CATE (conditional average treatment effects via T-learner + S-learner + X-learner + DR-learner + Causal Forest + BART). None runs synthetic control (Abadie-Gardeazabal + augmented), difference-in-differences (DiD + triple-differences + event study + Callaway-Sant’Anna + Goodman-Bacon decomposition), regression discontinuity, instrumental variables (2SLS), Heckman correction, propensity-score matching, inverse-propensity weighting, doubly-robust estimation, G-computation, or targeted maximum likelihood estimation (TMLE). None runs sensitivity analysis (Rosenbaum bounds + E-value + tipping-point + prior-sensitivity) to surface unmeasured-confounder exposure. None enforces FDD Item 19 FPR substantiation, Sarbanes-Oxley Section 302 / 404 / 906 certification, SEC Regulation G non-GAAP reconciliation, Robinson-Patman price-discrimination analysis, or the 30-state franchise relationship laws. The four-skill bundle Map + Estimate + Gate + Audit sits above the BI surface — it does not replace it. Map constructs the DAG. Estimate runs the causal stack. Gate enforces the regulatory anchors. Audit writes a per-unit per-driver WORM record.

What does Map do — DAG construction + Pearl do-calculus identification + driver catalog?

Map catalogs the 37 standing drivers across three layers (trade-area, unit, operational). Trade-area: demographics + foot traffic + real-estate cost + labor availability + competitor density + co-tenancy + traffic pattern + weather pattern + event calendar + seasonality + cross-banner cannibalization. Unit: tenure + multi-unit-operator status + franchisee experience + franchisee engagement + staff tenure + staff turnover + manager tenure + square footage + format + equipment vintage + remodel recency. Operational: loyalty penetration + AOV + conversion rate + channel mix + marketing-mix coefficient + pricing strategy + promotion cadence + product-mix margin + supply cost + labor cost + occupancy cost + royalty rate + marketing-fund contribution + FDD Item 19 FPR bucket. Map then constructs a directed-acyclic-graph (DAG) causal graph and runs Pearl do-calculus identification with back-door criterion, front-door criterion, instrumental-variable identification, mediator identification, collider identification, confounder identification. Each DAG edge carries a per-edge confidence tier and an explainability trace written into Audit. The DAG is the substrate that Estimate consumes.

What does Estimate do — Bayesian hierarchical + CATE + SCM + DiD + RD + IV + TMLE + counterfactual simulation?

Estimate runs the standing causal-inference stack on the DAG. Bayesian hierarchical multi-level model (via Stan + PyMC + NumPyro + bambi + brms-R + RStan + Edward2 + Pyro) respects unit-within-franchisee-within-region nesting. CATE estimation (T-learner + S-learner + X-learner + DR-learner using CausalML + DoubleML + EconML + Causal Forest + BART). Synthetic Control Method (Abadie-Gardeazabal + Augmented Synthetic Control + Doudchenko-Imbens + Ferman-Pinto). Difference-in-differences (DiD + triple-differences + event study + Callaway-Sant’Anna staggered-treatment + de Chaisemartin-D’Haultfoeuille + Goodman-Bacon decomposition). Regression Discontinuity Design (RD). Instrumental Variable (IV + 2SLS + GMM + Anderson-Hsiao + Arellano-Bond + Blundell-Bond). Heckman correction for selection bias. Propensity-score matching (nearest neighbor + kernel + stratified + radius). Inverse-propensity weighting (IPW + augmented IPW). Doubly-robust estimator. G-computation. Targeted Maximum Likelihood Estimation (TMLE). Sensitivity analysis (Rosenbaum bounds + E-value + tipping-point + prior-sensitivity). Counterfactual simulation runs per-driver scenarios (driver-up-1-stddev + up-2-stddev + down-1-stddev + down-2-stddev + to-system-median + to-top-decile + to-bottom-decile + to-target-bucket) and produces per-driver EBITDA delta + revenue delta + cost delta + margin delta + Bayesian posterior + Monte Carlo + bootstrap + quantile-regression prediction intervals + Borda-Condorcet rank aggregation + SHAP / LIME / PFI driver importance + intervention-cost-vs-EBITDA-uplift + ROI / IRR. Multi-LLM tie-breakers (OpenAI + Anthropic under per-vendor zero-retention) flag conflicts for operator review rather than auto-resolve. Per-driver confidence tier and explainability trace written into Audit.

What does Gate do — FDD Item 19 FPR + 30-state FRR + Sarbanes-Oxley + Reg G non-GAAP + Robinson-Patman + replication-crisis + EU AI Act Annex III?

Gate evaluates five operationally distinctive anchors before any per-unit per-driver decomposition is delivered. Anchor 1 (the most operationally distinctive — distinctive to franchise systems): FDD Item 19 financial performance representations per FTC Franchise Rule 16 CFR 436 + NASAA Commentary on FPRs — when EBITDA decomposition output reaches franchisee-facing FPR, the FPR must have reasonable basis + written substantiation + actual outlet data not projections + per-class disclosure + statistical-significance disclosures where applicable + FPR bucket + time window + cohort inclusion criteria + cohort exclusion criteria + substantiation document hash + source-of-record. Plus 15-state franchise registration (NY + CA + IL + MD + MI + MN + NB + ND + RI + SD + VA + WA + WI + HI + IN) + 7-additional-state franchise disclosure + 30-state franchise relationship laws + FDD Item 12 territorial-protection + FDD Item 17 renewal / termination / transfer. Anchor 2 (Sarbanes-Oxley + non-GAAP discipline): Sarbanes-Oxley Section 302 CEO + CFO certification of disclosure controls + Section 404 ICFR + auditor attestation per PCAOB AS 2201 + Section 906 criminal certification; SEC Regulation G 17 CFR 244 non-GAAP financial measures reconciliation + equal-or-greater-prominence + purpose disclosure + SEC C&DI Question 100 / 101 / 102 (4-wall EBITDA, Adjusted EBITDA, same-store-sales, comp-sales, cohort-LTV subject to Reg G when reaching public-co or PE-sponsor); SEC Reg S-K Item 303 MD&A; SEC Reg S-X; FASB ASC 606 revenue recognition 5-step (royalty + marketing-fund-contribution timing); FASB ASC 842 leases; FASB ASC 326 CECL; AICPA non-GAAP; PCAOB AS 2410. Anchor 3 (anti-trust + competition law): Robinson-Patman Act when per-driver pricing-strategy decomposition references competitive cost positions; Lanham Act 15 USC 1125(a) false-advertising; per-state UDAP; per-state Deceptive Trade Practices Act; state business-opportunity disclosure; state investment disclosure; CFPB UDAAP. Anchor 4 (replication-crisis statistical discipline): Rosenbaum bounds + E-value + tipping-point + prior-sensitivity for unmeasured-confounder exposure; multiple-comparisons correction (Bonferroni + Holm-Bonferroni + Benjamini-Hochberg FDR + Benjamini-Yekutieli + Tukey HSD + Dunnett + Scheffe) when reporting many per-driver decompositions; pre-registration (AsPredicted + OSF) when hypothesis-confirming. Anchor 5 (anti-discrimination + AI-governance): ECOA Regulation B disparate-impact + Fair Housing Act when per-unit decomposition correlates with protected class proxies; GDPR Article 22 + CCPA right to opt out of automated decision-making; EU AI Act Annex III high-risk classification (when AI-driven EBITDA decomposition drives capital deployment + franchisee-grant + termination decisions) + Article 9 risk-management + Article 13 + 14 + 15 + 22 + 26 + 50; NIST AI Risk Management Framework; ISO 42001 AI Management System; per-vendor LLM zero-retention verified per call. Policy-as-code expression via OPA Rego + AWS Cedar + Casbin + Cerbos + Oso + Styra DAS + Permit.io.

What does Audit do — per-unit per-driver WORM record + FBC feedback + end-to-end replay?

Audit writes a per-unit per-driver WORM record at every decomposition run + every counterfactual simulation: per-decomposition-run ID + per-banner pointer + per-unit pointer + 37-driver snapshot + DAG causal graph snapshot + Pearl do-calculus identification snapshot (back-door + front-door + instrumental-variable + mediator + collider + confounder) + Bayesian hierarchical snapshot + CATE snapshot + SCM + DiD + RD + IV + TMLE snapshot + sensitivity-analysis snapshot (Rosenbaum bounds + E-value + tipping-point + prior-sensitivity) + counterfactual scenario snapshot + per-driver EBITDA / revenue / cost / margin delta + Bayesian posterior + Monte Carlo + bootstrap + quantile-regression prediction intervals + Borda-Condorcet rank aggregation + SHAP / LIME / PFI driver importance + per-driver confidence tier + per-driver explainability + multi-LLM ensemble snapshot (per-vendor zero-retention verification) + per-anchor Gate decision with evidence (FDD Item 19 FPR substantiation + 30-state FRR posture + Sarbanes-Oxley 302 / 404 / 906 readiness + Reg G non-GAAP reconciliation + FASB ASC 606 alignment + Robinson-Patman + Lanham + replication-crisis correction record + Rosenbaum E-value + multiple-comparisons correction + pre-registration + ECOA disparate-impact + EU AI Act Annex III posture + Article 50 disclosure) + FBC feedback loop record (realized vs counterfactual EBITDA delta + realized vs counterfactual revenue / cost / margin delta + control vs treatment incrementality + geo-experiment validation + DAG edge recalibration + Bayesian prior recalibration + CATE recalibration + SCM / DiD / RD / IV recalibration + propensity-score recalibration + doubly-robust / TMLE recalibration + sensitivity-analysis recalibration + pattern learning + emerging-driver detection + model-drift detection + model-retraining trigger) + sibling-handoff pointers. Storage on AWS S3 Object Lock + Azure Blob immutable + Google Cloud Storage Bucket Lock + Wasabi compliance WORM. Retention stacks (longest applicable wins): 7-year SOX Section 802 + 7-year IRS tax + 7-year FTC substantiation + 7-year FDD record + per-state franchise registration + 6-year SEC + 5-year PCAOB + GDPR Article 30 + EU AI Act Article 12 + SOC 2 CC7 / CC8. End-to-end replay rewinds Map + Estimate + Gate + counterfactual simulation + FBC feedback with confidence tier and explainability at every stage. Sibling handoffs flow into the franchise-system-performance-correlation parent commercial pillar, the revenue drivers analysis sibling skill (sister-skill on the rollup-reporting agent for portfolio-level drivers analysis; this skill is its per-unit per-driver counterpart), peer-cohort benchmarking, franchisee accountability dashboard, per-prospect franchise-territory analysis pipeline sibling build-pillar, continuous per-market scoring sibling build-pillar, FDD territorial-protection gating sibling build-pillar, competitor-density territory mapping sibling build-pillar, continuous foot-traffic ingestion sibling build-pillar, per-franchisee accountability views sibling build-pillar, cohort-framed benchmark reports sibling build-pillar, peer-cohort computation sibling build-pillar, marketing forecasting sibling build-pillar, per-location multi-model attribution sibling build-pillar, per-location MMM and MMM-driven budget recommendation sibling build-pillars, root-cause attribution sketch sibling build-pillar, quarterly board deck generation, PE-sponsor LP letter drafting sibling build-pillar.

Engage Completions on the territory-analysis bundle

The Map + Estimate + Gate + Audit four-skill bundle ships as the orchestration layer above your existing BI + Bayesian + causal- inference + disclosure-management surface. FDD Item 19 FPR substantiation + 30-state franchise relationship laws + Sarbanes-Oxley + SEC Regulation G non-GAAP + FASB ASC 606 + Robinson-Patman + replication-crisis statistical discipline + ECOA disparate-impact + EU AI Act Annex III + NIST AI RMF anchors are preserved in every per-unit per-driver audit record. Tier 1 AI Readiness Assessment scopes the bundle in two to three weeks; Tier 3 Fractional CMO with AI Swarm operates the bundle end-to-end.

Tier 1 AI Readiness Assessment Tier 3 Fractional CMO with AI Swarm Take the 3-question quiz