The Policy Priority Inference (PPI) research programme aims at modelling the causal link between government expenditure and policy outcomes while accounting for the multidimensionality and complexity of development. It employs computational methods to overcome the limitations of coarse-grained data on development indicators and public spending. Multidimensionality means that development occurs, simultaneously, in a variety of policy issues, for example, poverty, public health, education, air pollution, etc. An example of a multidimensional development international agenda is the United Nations Sustainable Development Goals. Complexity arises when micro-level interventions generate macro-level outcomes as a result of the responses of the target population, the social norms emerging from their interactions, and the influence of public governance on the effectiveness of such interventions. At a more aggregate level, complexity also means that the various policy dimensions of development are interdependent. PPI has been adopted and promoted by various governments and international organisations in problems related to development and budget planning.