Training and Evaluating Causal Forecasting Models for Time-Series

Thomas Crasson; Yacine Nabet; Mathias Lécuyer

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arXiv · 2411.00126

Training and Evaluating Causal Forecasting Models for Time-Series

Thomas Crasson·Yacine Nabet·Mathias Lécuyer

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Abstract

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.

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Thomas Crasson, Yacine Nabet, Mathias Lécuyer. 2024-10-31. Training and Evaluating Causal Forecasting Models for Time-Series. https://arxiv.org/abs/2411.00126

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