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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.
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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