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

arXiv: https://arxiv.org/abs/2106.01315 · HTML: https://mlsanity.com/paper/2106.01315/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Learning Individual Causal Effects from Networked Observational Data](https://mlsanity.com/paper/1906.03485/) (2019)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [How Work From Home Affects Collaboration: A Large-Scale Study of Information Workers in a Natural Experiment During COVID-19](https://mlsanity.com/paper/2007.15584/) (2020)
- [Causal Modeling of Twitter Activity During COVID-19](https://mlsanity.com/paper/2005.07952/) (2020)

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks](https://mlsanity.com/paper/2407.19398/) (2024)
- [Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems](https://mlsanity.com/paper/2403.00178/) (2024)
- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.12400/) (2024)
- [CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events](https://mlsanity.com/paper/2412.02155/) (2024)
- [A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection](https://mlsanity.com/paper/2307.08237/) (2023)
- [A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View](https://mlsanity.com/paper/2409.09858/) (2024)
- [Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies](https://mlsanity.com/paper/2209.04631/) (2022)
- [Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications](https://mlsanity.com/paper/2209.14975/) (2022)
- [Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control](https://mlsanity.com/paper/2403.10744/) (2024)
- [Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data](https://mlsanity.com/paper/2410.15706/) (2024)
- [CRTRE: Causal Rule Generation with Target Trial Emulation Framework](https://mlsanity.com/paper/2411.06338/) (2024)
- [Causality-Aware Next Location Prediction Framework based on Human Mobility Stratification](https://mlsanity.com/paper/2503.18179/) (2025)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)
- [CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects](https://mlsanity.com/paper/2608.27942/) (2026)

## Abstract (arXiv)

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.