2019/11/02 by Haotian Zhong, Wei Li, Zhong, Haotian +3
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #Economic and Environmental Valuation #FOS: Economics and business #General Economics (econ.GN) #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.1911.00667
openalex publication_date 2019/11/02 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28
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 (RCTs) and match observations in both dimensions, to find synthetic control groups that are similar to the treatment group and to match subjects synthetically across before-treatment and after-treatment time periods. We call this a two-dimensional propensity score matching (2DPSM). This method demonstrates superior performance for estimating treatment effects based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the impact of transportation infrastructure on travel behavior.