研究时间序列干预效应可识别性，提出基于公共后门集的判定方法。
Identifiability by common backdoor in summary causal graphs of time series
- 基于摘要因果图，通过公共后门集判断多干预多效应的可识别性。
- 在时序一致与不一致场景下，给出后门集存在的充分条件。
- 提供低复杂度算法，适用于真实时间序列因果推断场景。
干预效应的可识别性问题旨在判断某组干预的总效应能否表示为无需do算子的公式，从而仅从观测数据中计算。本文在时间序列背景下，当仅有真实因果图的抽象形式——摘要因果图时，研究多干预、多效应的可识别性问题。重点关注通过公共后门集实现可识别性的条件，分别在时序具有一致性与无一致性的情形下，建立后门集存在性的确切条件，并设计了复杂度有限的判定算法，用于判断问题是否可识别。
原文摘要 · Abstract (English)
The identifiability problem for interventions aims at assessing whether the total effect of some given interventions can be written with a do-free formula, and thus be computed from observational data only. We study this problem, considering multiple interventions and multiple effects, in the context of time series when only abstractions of the true causal graph in the form of summary causal graphs are available. We focus in this study on identifiability by a common backdoor set, and establish, for time series with and without consistency throughout time, conditions under which such a set exists. We also provide algorithms of limited complexity to decide whether the problem is identifiable or not.
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