解决时间序列中永远无法观测对照组的问题，精准估算节日等事件影响。
Time Series Treatment Effects Analysis with Always-Missing Controls
- 通过重构事件期对照组，同时控制混杂因素与时间依赖性。
- 在M5沃尔玛数据上准确估计了节日效应，且结果具统计一致性。
- 适用于节日、政策等难以获取对照的因果推断场景。
时间序列中处理效应的估计面临重大挑战，尤其当对照组始终不可观测时。例如，在分析圣诞节对零售销售额的影响时，我们无法直接观测到没有圣诞影响的12月下旬实际情形。为解决这一问题，本文尝试在事件期内重构对照组，同时考虑混杂因素和时间依赖性。在M5沃尔玛零售销售数据上的实验表明，该方法能稳健估计对照组的潜在结果，并准确预测节假日效应。此外，本文提供了所估处理效应的理论保证，证明其一致性和渐近正态性。所提出的方法不仅适用于此类始终缺失对照的情况，也适用于其他常规的时间序列因果推断场景。
原文摘要 · Abstract (English)
Estimating treatment effects in time series data presents a significant challenge, especially when the control group is always unobservable. For example, in analyzing the effects of Christmas on retail sales, we lack direct observation of what would have occurred in late December without the Christmas impact. To address this, we try to recover the control group in the event period while accounting for confounders and temporal dependencies. Experimental results on the M5 Walmart retail sales data demonstrate robust estimation of the potential outcome of the control group as well as accurate predicted holiday effect. Furthermore, we provided theoretical guarantees for the estimated treatment effect, proving its consistency and asymptotic normality. The proposed methodology is applicable not only to this always-missing control scenario but also in other conventional time series causal inference settings.
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