Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
时间序列政策决策中的计量方法与因果结构学习：英国新冠政策证据AI 解读 ↓
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时间序列政策决策中的计量方法与因果结构学习：英国新冠政策证据
- 研究问题
- 计量方法与因果机器学习在时间序列结构发现上有何差异，这些差异如何影响英国新冠政策案例中的政策决策支持？
- 方法与思路
- 作者比较四种计量方法与十一种因果机器学习算法，考察图结构、模型维度和因果效应恢复，并提供转接 bnlearn 的代码。
- 材料陈述的贡献
- 摘要称计量方法提供清晰的时间结构规则，因果机器学习探索更广图空间并倾向产生更稠密图，报告了两类方法的差异。
- 阅读关注点
- 应核对各方法的时间与识别限制、真实数据中效应恢复的评价依据，以及图稠密程度与可识别因果关系如何区分。
- 阅读边界
- 这是 arXiv 预印本，不能确认已评审或接收；摘要未列明算法与识别条件，更稠密图本身不能证明发现了更多真实因果关系。
原文摘要
Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time series data remains the subject of ongoing research in causal ML. In addition to traditional causal ML, this study assesses econometric methods that some argue can recover causal structures from time series data. The use of these methods can be explained by the significant attention the field of econometrics has given to causality, and specifically to time series, over the years. This presents the possibility of comparing the causal discovery performance between econometric and traditional causal ML algorithms. We seek to understand if there are lessons to be incorporated into causal ML from econometrics, and provide code to translate the results of these econometric methods to the most widely used Bayesian Network R library, bnlearn. We investigate the benefits and challenges that these algorithms present in supporting policy decision-making, using the real-world case of COVID-19 in the UK as an example. Four econometric methods are evaluated in terms of graphical structure, model dimensionality, and their ability to recover causal effects, and these results are compared with those of eleven causal ML algorithms. Amongst our main results, we see that econometric methods provide clear rules for temporal structures, whereas causal-ML algorithms offer broader discovery by exploring a larger space of graph structures that tends to lead to denser graphs that capture more identifiable causal relationships.