Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases exponentially with longer prediction horizons. Thus, the observed data contain little support for many plausible treatment sequences, which creates severe overlap problems. Existing meta-learners for the time-varying setting typically assume adequate treatment overlap, and thus suffer from exploding estimation variance when the overlap is low. To address this problem, we introduce a novel overlap-weighted orthogonal (WO) meta-learner for estimating HTEs that targets regions in the observed data with high probability of receiving the interventional treatment sequences. This offers a fully data-driven approach through which our WO-learner can counteract instabilities as in existing meta-learners and thus obtain more reliable HTE estimates. Methodologically, we develop a novel Neyman-orthogonal population risk function that minimizes the overlap-weighted oracle risk. We show that our WO-learner has the favorable property of Neyman-orthogonality, meaning that it is robust against misspecification in the nuisance functions. Further, our WO-learner is fully model-agnostic and can be applied to any machine learning model. Through extensive experiments with both transformer and LSTM backbones, we demonstrate the benefits of our novel WO-learner.

翻译：在时变环境中估计异质性处理效应尤为困难，因为观察到特定处理序列的概率随预测时域的延长呈指数下降。因此，观测数据对许多合理处理序列的支持度极低，从而产生严重的重叠问题。现有适用于时变场景的元学习器通常假设处理重叠充分，因此在重叠度较低时会出现估计方差爆炸的问题。为解决此问题，我们提出了一种新颖的基于重叠加权的正交元学习器，用于估计异质性处理效应，该学习器以观测数据中接受干预处理序列概率较高的区域为目标。这提供了一种完全数据驱动的方法，使我们的WO学习器能够抵消现有元学习器中的不稳定性，从而获得更可靠的HTE估计。在方法论上，我们提出了一种新颖的奈曼正交总体风险函数，该函数最小化重叠加权的理想风险。我们证明了所提出的WO学习器具有奈曼正交性的优良特性，这意味着其对干扰函数的误设具有鲁棒性。此外，我们的WO学习器完全与模型无关，可应用于任何机器学习模型。通过使用Transformer和LSTM骨干网络进行大量实验，我们验证了所提出的新型WO学习器的优势。