Bootstrap aggregation and confidence measures to improve time series causal discovery
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利用 Bootstrap 集成与置信度度量改进时间序列因果发现
- 研究问题
- 多变量时间序列因果图学习如何在保留时间依赖和滞后结构的同时，评估图中连接的不确定性，并通过集成改善结构发现表现？
- 方法与思路
- 作者提出保留时间依赖与滞后结构的 Bootstrap，构造连接置信度，并以多数投票集成重抽样因果图，具体结合 PCMCI+ 进行实验。
- 材料陈述的贡献
- 摘要称，Bagged-PCMCI+ 在数值实验中提高精确率和召回率，短样本、多变量及高自相关情境的改善更明显，但需要更多计算资源。
- 阅读关注点
- 应核对重抽样如何保存时间结构、连接置信度的统计含义、投票阈值及因果识别条件，并比较性能增益与计算成本的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实。摘要未给出理论校准或具体性能数值，连接置信度不能直接当作正式置信区间或因果证明。
原文摘要
Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, or engineering, to name a few. Recent developments for this causal discovery learning task have shown considerable skill, notably the specific time-series adaptations of the popular conditional independence-based learning framework. However, uncertainty estimation is challenging for conditional independence-based methods. Here, we introduce a novel bootstrap approach designed for time series causal discovery that preserves the temporal dependencies and lag structure. It can be combined with a range of time series causal discovery methods and provides a measure of confidence for the links of the time series graphs. Furthermore, next to confidence estimation, an aggregation, also called bagging, of the bootstrapped graphs by majority voting results in bagged causal discovery methods. In this work, we combine this approach with the state-of-the-art conditional-independence-based algorithm PCMCI+. With extensive numerical experiments we empirically demonstrate that, in addition to providing confidence measures for links, Bagged-PCMCI+ improves in precision and recall as compared to its base algorithm PCMCI+, at the cost of higher computational demands. These statistical performance improvements are especially pronounced in the more challenging settings (short time sample size, large number of variables, high autocorrelation). Our bootstrap approach can also be combined with other time series causal discovery algorithms and can be of considerable use in many real-world applications.