Assessing the Causal Impact of COVID-19 Related Policies on Outbreak
Dynamics: A Case Study in the US
- URL: http://arxiv.org/abs/2106.01315v1
- Date: Sat, 29 May 2021 00:40:24 GMT
- Title: Assessing the Causal Impact of COVID-19 Related Policies on Outbreak
Dynamics: A Case Study in the US
- Authors: Jing Ma, Yushun Dong, Zheng Huang, Daniel Mietchen, Jundong Li
- Abstract summary: Analyzing the causal impact of non-pharmaceutical 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)
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.
- Score: 19.901831180866132
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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