Covariate selection in propensity score matching: A case study of how the Shinkansen has impacted population changes in Japan

Abstract

This study presents a novel covariate selection method that combines Maximum Likelihood Estimation (MLE) with the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), focusing on achieving a balance between model fit and complexity. Our findings emphasize the significant impact of covariates on estimated results in Propensity Score Matching (PSM) analyses. Through case studies, we validate the effectiveness of our proposed method across various PSM approaches, including one-to-one matching, K-nearest neighbors matching, radius matching, kernel matching, and Inverse Probability Weighting (IPW). For researchers constrained to cross-sectional data, our comparisons among different PSM methodologies provide valuable insights. Additionally, we explore the applicability of our method to PSM extensions such as Covariate Balancing Propensity Score (CBPS) and PSM-Difference-in-Differences (DID). Our case study reveals significant causal effects of Japan’s Shinkansen on population changes, with notable growth observed in both cross-sectional and panel data analyses.These findings hold important implications for transportation policy, and we offer recommendations for relevant policymakers based on our results.