Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative.

This article explores some of the challenges that arise when trying to implement propensity score strategies to answer a causal question using data with a large number of covariates. We discuss choices in propensity score estimation strategies, matching and weighting implementation strategies, balance diagnostics, and final analysis models. We demonstrate the wide range of estimates that can result from different combinations of these choices. Finally, an alternative estimation strategy is presented that may have benefits in terms of simplicity and reliability. These issues are explored in the context of an empirical example that uses data from the Early Childhood Longitudinal Study, Kindergarten Cohort to investigate the potential effect of grade retention after the 1st-grade year on subsequent cognitive outcomes.