A Modified General Location Model for Noncompliance With Missing Data

Missing data, especially when coupled with noncompliance, are a challenge even in the setting of randomized experiments. Although some existing methods can address each complication, it can be difficult to handle both of them simultaneously. This is true in the example of the New York City School Choice Scholarship Program, where both the covariates and the outcomes were sometimes missing, and there was complicated noncompliance. The authors propose a modified general location model to integrate the ideas of missing data techniques and principal stratification and then analyze the same data as in Barnard, Frangakis, Hill, and Rubin (2003), where a pattern-mixture model was used. Their results are presented and compared with those in Barnard et al.