“Missingness Graphs” (m-graphs) are causal graphical models used for processing missing data. They portray the causal mechanisms responsible for missingness and thus encode knowledge about the underlying process that generates data. Using m-graphs, we develop methods to determine if there exists a consistent estimator for a given quantity of interest such as joint distributions, conditional distributions and causal effects. Our methods apply to all types of missing data including the notorious and relatively unexplored NMAR (Not Missing At Random) category. We further address the question of testability i.e. if and how an assumed model can be subjected to statistical tests, considering the missingness in the data. Viewing the missing data problem from a causal perspective has ushered in several surprises such as recoverability when variables are causes of their own missingness, testability of MAR models and the indispensability of causal assumptions for handling missing data problems.