提出因果时间序列模型，提升决策外泛化能力。
Training and Evaluating Causal Forecasting Models for Time-Series
- 基于因果推断框架训练模型，增强对干预外的预测能力
- 使用断点回归设计构建测试集，评估外部干预效果
- 适合需要应对真实世界策略变化的预测场景
深度学习时间序列模型常用于支持下游决策，但这些决策可能超出训练数据分布。当前模型多在分布内任务上训练与评估，难以保证外分布泛化能力。本文将正交统计学习框架扩展至时间序列，训练能更好外推因果效应的模型。为评估其性能，借鉴经济学中流行的断点回归设计（Regression Discontinuity Design），构建包含因果处理效应的测试集，从而更真实地检验模型在非训练干预下的表现。
原文摘要 · Abstract (English)
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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