提出时间序列干预可识别性的完整判定方法
Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
- 基于摘要因果图构建调整准则的完备条件
- 给出判断干预效应是否可从观测数据估计的算法
- 适用于多干预场景的时间序列因果推断
干预可识别性问题旨在判断总因果效应能否表示为无需do算子的公式，从而仅通过观测数据进行估计。本文研究在时间序列背景下，当仅有真实因果图的抽象形式——摘要因果图时，面对多重干预情形下的该问题。我们提出了调整准则的必要且充分条件，并证明其在该设置下是完备的；同时提供一种伪线性算法，用于判断查询是否可识别。
原文摘要 · Abstract (English)
The identifiability problem for interventions aims at assessing whether the total causal effect can be written with a do-free formula, and thus be estimated from observational data only. We study this problem, considering multiple interventions, in the context of time series when only an abstraction of the true causal graph, in the form of a summary causal graph, is available. We propose in particular both necessary and sufficient conditions for the adjustment criterion, which we show is complete in this setting, and provide a pseudo-linear algorithm to decide whether the query is identifiable or not.
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