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I examine the common problem of multiple missingness on both the endogenoustreatment and outcome variables. Two types of dependence assumptions formissing mechanisms are proposed for identification, based on which a two-stepAIPW GMM estimator is proposed. This estimator is unbiased and more efficientthan the previously used estimation methods. Statistical properties arediscussed case by case. This method is applied to the Oregon Health InsuranceExperiment and shows the significant effects of enrolling in the Oregon HealthPlan on improving health-related outcomes and reducing out-of-pocket costs formedical care. There is evidence that simply dropping the incomplete datacreates downward biases for some of the chosen outcome variables. Moreover, theestimator proposed in this paper reduced standard errors by 6-24% of theestimated effects of the Oregon Health Plan.

A GMM Approach for Non-monotone Missingness on Both Treatment and Outcome Variables