  Can humans impute missing data with similar proficiency as machines? This is
the question we aim to answer in this paper. We present a novel idea of
converting observations with missing data in to a survey questionnaire, which
is presented to crowdworkers for completion. We replicate a multiple imputation
framework by having multiple unique crowdworkers complete our questionnaire.
Experimental results demonstrate that using our method, it is possible to
generate valid imputations for qualitative and quantitative missing data, with
results comparable to imputations generated by complex statistical models.
