Nonnegative tensor completion for dynamic counterfactual prediction on COVID-19 pandemic
The Annals of Applied Statistics · 2024 · Volume 18 · Issue 1
Abstract
The COVID-19 pandemic has been a worldwide health crisis for the past three years, casting unprecedented challenges for policymakers in different countries and regions. While one country or region can only implement one social mobility restriction policy at a given time, it is of great interest for policy makers to decide whether to elevate or deelevate the restriction policy from time to time. This article proposes a novel nonnegative tensor completion method to predict the potential counterfactual outcomes of multifaceted social mobility restriction policies over time. The proposed method builds upon a low-rank tensor decomposition of the pandemic data, which also explicitly characterizes the ordinal nature of the mobility restriction strength and the smooth trend of the pandemic evolution over time. Its application to the COVID-19 pandemic data reveals some interesting facts regarding the impact of social mobility restriction policy on the spread of the virus. The effectiveness of the proposed method is also supported by its asymptotic estimation consistency and extensive numerical experiments on the synthetic datasets.
Keywords
Causal inference, imputation, informative missing, latent factor, tensor decomposition
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