  We study heterogeneity in the effect of a mindset intervention on
student-level performance through an observational dataset from the National
Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to
address the following associated problems: assessing treatment group overlap
and covariate balance, imputing conditional average treatment effects, and
interpreting imputed effects. By comparing several different model families we
illustrate 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] is
moderated by school-level achievement level, poverty concentration, urbanicity,
and student prior expectations.
