Powerbi Causal Counterfactual Forecasting

Power BI Causal Counterfactual Forecasting

Use this skill when the user asks why a forecast changes, what causes a revenue gap, or what would happen under alternative assumptions.

Feature families

• calendar: working days, holidays, month length, fiscal periods
• order flow: order age, requested delivery, planned delivery, status, backlog value
• customer behavior: recency, frequency, average order value, churn or reactivation signals
• product lifecycle: new product, mature product, discontinued product, replacement product
• operations: supply constraints, delivery delay, stockout indicators
• commercial: price change, discounting, campaign, sales initiative, budget/roll assumptions

Workflow

1. Separate correlation from actionable cause. Do not claim causality without a plausible mechanism and supporting time sequence.
2. Build counterfactuals:
   • if backlog conversion improves
   • if delivery slips
   • if customer demand follows prior year
   • if budget pressure is ignored
   • if low-confidence segments are excluded
3. Quantify sensitivity:
   • revenue impact
   • probability
   • confidence
   • affected customer/product/month
4. Explain the causal story in one sentence per material driver.

Required outputs

• forecast_month
• driver
• driver_type
• base_value
• counterfactual_value
• revenue_impact
• confidence
• evidence
• actionability

Guardrails

• Mark drivers as hypothesis when the data only supports association.
• Avoid overfitting small customer/product segments.
• Use backtests to prove that adding a driver improves WAPE or bias before making it a default weight.