Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods

Abstract

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has an extensive literature documenting positive effects on infant health outcomes, specifically preterm birth, low birthweight, small size for gestational age, and infant mortality. However, existing studies focus on average effects for these relatively infrequent outcomes, thus providing no evidence for how WIC affects those at greatest risk of negative infant health outcomes. Our study focuses on documenting how WIC's infant health effects vary by level of risk. In doing so, we leverage a uniquely rich database describing maternal and infant outcomes and risk factors. Additionally, we use high dimensional data to generate predictions of risk and combine these predictions with the novel double machine learning method to stratify the effects of WIC by predicted risk. Our estimates of WIC's average treatment effects align with those in the existing literature. More importantly, we document significant variation in the effects of WIC on infant health by predicted risk level. Our results show that WIC is most beneficial among those at greatest risk of poor outcomes.