- High prices often appear with high demand, not because price increases demand, but because both were driven by market conditions.
- A model optimized for prediction error will learn this biased relationship and can misestimate price elasticity.
- The result: a model can score well on RMSE yet behave poorly when used to simulate price changes.
When Forecasting Isn’t Enough: Why Causal Time‑Series Models Matter
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Why classic forecasting breaks in real pricing systems
In most industries, prices are set deliberately. When demand is expected to be high, prices rise. When demand is expected to be weak, prices fall. That means historical data mixes true demand dynamics with the pricing strategy that generated the data.
- High prices often appear with high demand, not because price increases demand, but because both were driven by market conditions.
- A model optimized for prediction error will learn this biased relationship and can misestimate price elasticity.
- The result: a model can score well on RMSE yet behave poorly when used to simulate price changes.
Transition from forecasting to decision intelligence
We train models to separate baseline demand dynamics from treatment effects, using an orthogonal learning framework adapted to deep time‑series architectures. Then we evaluate causal performance using benchmark moments where prices change sharply while conditions remain comparable.
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