Seminar 2025-01-29: David Randahl

Speaker David Randahl, Department of Peace and Conflict Research, Uppsala University

Topic Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict

Abstract Armed conflict forecasting is an important area of research that has the potential to save lives and prevent suffering. However, most existing forecasting models provide only point predictions without any individual-level uncertainty estimates. In this paper, we propose a novel extension to conformal prediction which allows users to obtain individual-level prediction intervals for any arbitrary prediction model and which maintains a user-specified level of coverage across bins of values defined by the user. We then apply this bin-conditional conformal prediction model to forecast fatalities from armed conflict. The results show that the bin-conditional conformal prediction model provides well-calibrated uncertainty estimates for the predicted number of fatalities. The bin-conditional method outperforms the standard conformal prediction method with respect to thecalibration of coverage rates across different values of the prediction outcome, but at the cost of wider prediction intervals.