Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US
评估新冠相关政策对疫情动态的因果影响：美国案例AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用县级纵向观察数据估计政策因果效应。
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评估新冠相关政策对疫情动态的因果影响：美国案例
- 研究问题
- 美国县级新冠政策与疫情动态之间存在随时间变化的未观测混杂时，如何估计不同时段、不同政策类别及具体政策的因果影响？
- 方法与思路
- 作者整合县级政策和疫情的纵向数据，利用县域协变量、关系及历史信息，通过神经网络学习时变未观测混杂因素的表示。
- 材料陈述的贡献
- 摘要称该框架支持多层次政策效应评估，并在实验中更准确刻画疫情动态，所得政策评估与既有流行病学研究更为一致。
- 阅读关注点
- 应核对混杂表示能支持因果识别的依据、政策时序和共同实施的处理，以及关系信息的作用与因果评估的验证方式。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明识别假设，预测更准或与既有研究一致不足以验证因果效应。
原文摘要
To mitigate the spread of COVID-19 pandemic, decision-makers and public authorities have announced various non-pharmaceutical policies. Analyzing the causal impact of these policies in reducing the spread of COVID-19 is important for future policy-making. The main challenge here is the existence of unobserved confounders (e.g., vigilance of residents). Besides, as the confounders may be time-varying during COVID-19 (e.g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them. In this paper, we study the problem of assessing the causal effects of different COVID-19 related policies on the outbreak dynamics in different counties at any given time period. To this end, we integrate data about different COVID-19 related policies (treatment) and outbreak dynamics (outcome) for different United States counties over time and analyze them with respect to variables that can infer the confounders, including the covariates of different counties, their relational information and historical information. Based on these data, we develop a neural network based causal effect estimation framework which leverages above information in observational data and learns the representations of time-varying (unobserved) confounders. In this way, it enables us to quantify the causal impact of policies at different granularities, ranging from a category of policies with a certain goal to a specific policy type in this category. Besides, experimental results also indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of different policies. More specifically, compared with several baseline methods, our framework captures the outbreak dynamics more accurately, and our assessment of policies is more consistent with existing epidemiological studies of COVID-19.