Intertemporal propensity score matching for casual inference: an application to covid-19 lockdowns and air pollution in Northern Italy

This paper develops an intertemporal propensity score matching (PSM) approach for estimating the impact of covid-19 lockdowns on air pollution. While PSM has been exclusively applied in the context of matching cross-sectional units, this paper shows that, under specific circumstances, PSM can be also applied for estimating causal inference by means of matching across different temporal units in the context of multivariate time series data.We apply our intertemporal PSM model to the data collected from a large number of air-pollution-measurement stations in Northern Italy, estimating the casual effect of the March-May-2020 lockdown on air-pollution without resorting to the more stringent functional form assumptions of the existing literature