Multilevel structural equation modeling-based quasi-experimental synthetic cohort design

This paper provides a theoretical foundation to examine the effectiveness of post-hoc adjustment approaches such as propensity score matching in reducing the selection bias of synthetic cohort design (SCD) for causal inference and program evaluation. Compared with the Solomon four-group design, the SCD often encounters selection bias due to the imbalance of covariates between the two cohorts. The efficiency of SCD is ensured by the historical equivalence of groups (HEoG) assumption, indicating the comparability between the two cohorts. The multilevel structural equation modeling framework is used to define the HEoG assumption. According to the mathematical proof, HEoG ensures that the use of SCD results in an unbiased estimator of the schooling effect. Practical considerations and suggestions for future research and use of SCD are discussed.