Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

翻译：从观测数据中进行反事实估计是医疗和金融等众多应用领域的关键任务，其主要挑战在于缓解处理偏差。旨在减少不同处理组间协变量差异的平衡策略是一种通用解决方案。然而，在时间序列数据场景中，平衡策略的有效性仍是一个开放性问题，且目前仍缺乏对其鲁棒性与适用性的系统分析。本文重新审视时序背景下的反事实估计问题，简要概述了平衡策略的最新进展。更重要的是，我们在多个数据集的不同设定下，对时序反事实估计领域中平衡策略的有效性进行了批判性实证检验。我们的研究结果对学术界与工业界研究者具有重要参考价值，并呼吁在时间序列场景中重新审视平衡策略的有效性。