Heterogeneous Subgroup Identification with Observational Data: A Case Study Based on the National Study of Learning Mindsets

Abstract

Abstract:In this paper, we use a two-step approach for heterogeneous subgroup identification with a synthetic data set motivated by the National Study of Learning Mindsets. In the first step, optimal full propensity score matching is used to estimate stratum-specific treatment effects. In the second step, regression trees identify key subgroups based on covariates for which the treatment effect varies. In working with regression trees, we emphasize the role of the cost-complexity tuning parameter, selected through permutation-based Type I error rate studies, in justifying inferential decision-making, which we contrast with graphical and quantitative exploration for future study. Results indicate that the mindset intervention was effective, overall, in improving student achievement. While our exploratory analyses identified XC, C1, and X1 as potential effect modifiers worthy of further study, we find no statistically significant evidence of effect heterogeneity with the exception of urbanicity category XC = 3, but the finding is not robust to propensity score estimation method.