What-If Simulator
Causal intervention modelling · DoubleML CATE · results update instantly
No scenario — adjust sliders to begin
Adjust the sliders to model the causal impact of pricing, marketing, headcount, and rate changes on revenue, margin, and cash.
Intervention Levers
Price Change
Average selling price delta across all SKUs
Marketing Spend
Change in quarterly marketing budget
Headcount
Workforce size change (+ = hire, − = reduce)
BoC Rate Shock
Basis points change to overnight rate
Baseline vs Scenario
Scenario Revenue
$4.22M
↑ +0.0% vs base
Base: $4.22M
Gross Margin
34.2%
↑ +0.0pp vs base
Base: 34.2%
EBITDA
$1.06M
→ $-0.12M
Base: $1.18M
Debt Service
$380K
↑ +$0K vs base
Base: $380K / quarter
Cash Position
$6.97M
↑ +$0.00M
Base: $6.97M
Scenario Comparison
Baseline · Current Scenario · Stress (Combined)
Revenue Impact Decomposition
Revenue Bridge — Baseline to Scenario
Contribution of each lever · $M quarterly
Sensitivity Tornado
Revenue impact of each lever at max setting · $M swing
Price is the highest-leverage lever. Marketing shows diminishing returns above +30%.
Causal deltas estimated via DoubleML Partially Linear Regression (PLR). Treatment variables: price delta %, marketing spend delta %, headcount delta %, BoC rate bps. Outcome: quarterly revenue and gross margin. Confounders: macro cycle phase, seasonality index, customer concentration. CATE coefficients pre-computed — ONNX worker wires in live inference in Session 12.