A two-dimensional propensity score matching method for longitudinal quasi-experimental studies: A focus on travel behavior and the built environment

The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials and match observations in both dimensions to find synthetic control groups that are similar to the treatment group and to match subjects across before- and after-treatment periods. We call this a two-dimensional propensity score matching method. This method demonstrates superior performance for improving treatment effect estimation based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the treatment effect of transportation infrastructure on travel behavior.