Investigating the Potential of Machine Learning Methods for Identifying Impact Variation in Randomized Control Trials

In research on the effects of education interventions, there is interest not only in the overall average treatment effect but also in effects for subgroups of sample members and whether those effects differ. The purpose of this grant is to investigate the value and limitations of using machine learning to detect the presence of heterogeneous subgroup impacts in randomized control trials (RCTs) in education and other policy domains. The machine learning approach does not require researchers to pre-specify subgroups, as is typically important when using standard multiple comparison procedures, and machine learning can be used when there are a large number of candidate characteristics to examine.