Stay Home Save Lives: A Machine Learning Approach to Causal Inference to Evaluate Impact of Social Distancing in the US

Although there are few studies done to provide estimations of the impact of COVID-19 pandemic, however, there is a need for an actual policy evaluation of the already implemented social distancing measures. In the US context in specific, this is especially instrumental because nearly a dozen US states are considering the reopening of the economy following anti social distancing protests. Using a machine learning based Generalized Synthetic Control Method, considering the US states that adopted early social distancing approaches as the treatment group and the states that adopted social distancing much later as the control group and controlling for state and time fixed effects (to cancel out the selection bias and endogeneity), this paper finds that social distancing is associated with lower COVID-19 infection growth rate (by 192%) when compared to the no policy intervention counterfactual.

尽管现有针对新冠疫情（COVID-19 pandemic）影响的量化估算研究较为匮乏，但针对已落地的社交距离措施开展实际政策评估仍极具必要性。具体到美国语境中，这一需求尤为关键——近十二个美国州在爆发反对社交距离的抗议活动后，正考虑重启经济。本文采用基于机器学习的广义合成控制法（Generalized Synthetic Control Method），将较早推行社交距离措施的美国州设为处理组，较晚推行该措施的州设为对照组，并控制州层面与时间层面的固定效应以消除选择性偏差与内生性问题。研究结果表明，相较于无政策干预的反事实情景，社交距离措施可使新冠感染增长率降低192%。