提出首个时间序列因果识别的有限计算边界，可高效判断远距离因果效应是否可识别。
Causal Identification in Time Series Models
- 基于固定大小的时间片段进行因果识别，无需无限长序列
- 仅依赖每时段变量数和最大时滞，给出可识别性判定的理论上限
- 适用于存在隐含混杂因素的复杂时序系统，对因果推断研究者有重要价值
本文研究因果识别算法在含隐含混杂因素的时间序列图中的适用性。由于这类图跨越无限多个时间步，判断任意时间间隔上的因果效应是否可识别，看似需要处理无界大小的图片段。即使对于时间上相近变量的干预效应，也尚无已知的过去时间步数上限。本文首次给出了此类界限，其大小仅依赖于每时段变量数和任意直接或隐含因果效应的最大时滞。更一般地，我们证明：只需对时间序列图的一个常数大小片段应用因果识别算法，即可决定跨无界时间间隔的因果效应是否可识别。
原文摘要 · Abstract (English)
In this paper, we analyze the applicability of the Causal Identification algorithm to causal time series graphs with latent confounders. Since these graphs extend over infinitely many time steps, deciding whether causal effects across arbitrary time intervals are identifiable appears to require computation on graph segments of unbounded size. Even for deciding the identifiability of intervention effects on variables that are close in time, no bound is known on how many time steps in the past need to be considered. We give a first bound of this kind that only depends on the number of variables per time step and the maximum time lag of any direct or latent causal effect. More generally, we show that applying the Causal Identification algorithm to a constant-size segment of the time series graph is sufficient to decide identifiability of causal effects, even across unbounded time intervals.
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