  Due to the cost or interference of measurement, we need to control
measurement system. Assuming that each variable can be measured sequentially,
there exists optimal policy choosing next measurement for the former
observations. Though optimal measurement policy is actually dependent on the
goal of measurement, we mainly focus on retrieving complete data, so called as
imputation. Also, we adapt the imputation method to missingness varying with
measurement policy. However, learning measurement policy and imputation
requires complete data which is impossible to be observed, unfortunately. To
tackle this problem, we propose a data generation method and joint learning
algorithm. The main idea is that 1) the data generation method is inherited by
imputation method, and 2) the adaptation of imputation encourages measurement
policy to learn more than individual learning. We implemented some variations
of proposed algorithm for two different datasets and various missing rates.
From the experimental results, we demonstrate that our algorithm is generally
applicable and outperforms baseline methods.
