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We study heterogeneity in the effect of a mindset intervention onstudent-level performance through an observational dataset from the NationalStudy of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) toaddress the following associated problems: assessing treatment group overlapand covariate balance, imputing conditional average treatment effects, andinterpreting imputed effects. By comparing several different model families weillustrate the flexibility of both off-the-shelf and purpose-built estimators.We find that the mindset intervention has a positive average effect of 0.26,95%-CI [0.22, 0.30], and that heterogeneity in the range of [0.1, 0.4] ismoderated by school-level achievement level, poverty concentration, urbanicity,and student prior expectations.

Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions