Predicting onsets of genocide with sparse additive models

Prevention of genocide is one of the most important challenges before the international community. In this paper we apply recent machine learning techniques to forecast the onset of political instability and genocide. Specifically, we employ sparse additive models which are both flexible and maintain interpretability of the results. Our model demonstrates a reasonable degree of forecasting performance over the hold-out period 1988-2003.