Learning Bayesian Networks without Assuming Missing at Random. (arXiv:1608.07734v1 [cs.AI])

We present new algorithms for learning Bayesian networks from data with missing values without the assumption that data are missing at random (MAR). An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create a new approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks without assuming MAR.

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