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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.00126 |
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| _version_ | 1866909374384963584 |
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| author | Crasson, Thomas Nabet, Yacine Lécuyer, Mathias |
| author_facet | Crasson, Thomas Nabet, Yacine Lécuyer, Mathias |
| contents | Deep learning time-series models are often used to make forecasts that inform downstream decisions. Since these decisions can differ from those in the training set, there is an implicit requirement that time-series models will generalize outside of their training distribution. Despite this core requirement, time-series models are typically trained and evaluated on in-distribution predictive tasks. We extend the orthogonal statistical learning framework to train causal time-series models that generalize better when forecasting the effect of actions outside of their training distribution. To evaluate these models, we leverage Regression Discontinuity Designs popular in economics to construct a test set of causal treatment effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Training and Evaluating Causal Forecasting Models for Time-Series Crasson, Thomas Nabet, Yacine Lécuyer, Mathias Machine Learning Artificial Intelligence Deep learning time-series models are often used to make forecasts that inform downstream decisions. Since these decisions can differ from those in the training set, there is an implicit requirement that time-series models will generalize outside of their training distribution. Despite this core requirement, time-series models are typically trained and evaluated on in-distribution predictive tasks. We extend the orthogonal statistical learning framework to train causal time-series models that generalize better when forecasting the effect of actions outside of their training distribution. To evaluate these models, we leverage Regression Discontinuity Designs popular in economics to construct a test set of causal treatment effects. |
| title | Training and Evaluating Causal Forecasting Models for Time-Series |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.00126 |