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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2312.15282 |
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| _version_ | 1866909087025856512 |
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| author | Schultz, Douglas Stephan, Johannes Sieber, Julian Yeh, Trudie Kunz, Manuel Doupe, Patrick Januschowski, Tim |
| author_facet | Schultz, Douglas Stephan, Johannes Sieber, Julian Yeh, Trudie Kunz, Manuel Doupe, Patrick Januschowski, Tim |
| contents | This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models. In extensive empirical experiments, we show on the one hand that our method estimates the causal effect better in a fully controlled setting via synthetic, yet realistic data. On the other hand, we demonstrate on real-world data that our method outperforms forecasting methods in off-policy settings (i.e., when there's a change in the pricing policy) while only slightly trailing in the on-policy setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15282 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Causal Forecasting for Pricing Schultz, Douglas Stephan, Johannes Sieber, Julian Yeh, Trudie Kunz, Manuel Doupe, Patrick Januschowski, Tim Machine Learning This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models. In extensive empirical experiments, we show on the one hand that our method estimates the causal effect better in a fully controlled setting via synthetic, yet realistic data. On the other hand, we demonstrate on real-world data that our method outperforms forecasting methods in off-policy settings (i.e., when there's a change in the pricing policy) while only slightly trailing in the on-policy setting. |
| title | Causal Forecasting for Pricing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2312.15282 |