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To mitigate the spread of COVID-19 pandemic, decision-makers and publicauthorities have announced various non-pharmaceutical policies. Analyzing thecausal impact of these policies in reducing the spread of COVID-19 is importantfor future policy-making. The main challenge here is the existence ofunobserved confounders (e.g., vigilance of residents). Besides, as theconfounders may be time-varying during COVID-19 (e.g., vigilance of residentschanges in the course of the pandemic), it is even more difficult to capturethem. In this paper, we study the problem of assessing the causal effects ofdifferent COVID-19 related policies on the outbreak dynamics in differentcounties at any given time period. To this end, we integrate data aboutdifferent COVID-19 related policies (treatment) and outbreak dynamics (outcome)for different United States counties over time and analyze them with respect tovariables that can infer the confounders, including the covariates of differentcounties, their relational information and historical information. Based onthese data, we develop a neural network based causal effect estimationframework which leverages above information in observational data and learnsthe representations of time-varying (unobserved) confounders. In this way, itenables us to quantify the causal impact of policies at differentgranularities, ranging from a category of policies with a certain goal to aspecific policy type in this category. Besides, experimental results alsoindicate the effectiveness of our proposed framework in capturing theconfounders for quantifying the causal impact of different policies. Morespecifically, compared with several baseline methods, our framework capturesthe outbreak dynamics more accurately, and our assessment of policies is moreconsistent with existing epidemiological studies of COVID-19.

Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US