Prediction, Proxies, and Power

American Journal of Political Science, 2019 (with Robert J. Carroll).

Download the paper here.

The latest DOE scores are available at Source code is available on GitHub. Replication code for the original AJPS article is available on Dataverse.

Abstract. Many enduring questions in international relations theory focus on power relations, so it is important that scholars have a good measure of relative power. The standard measure of relative military power, the capability ratio, is barely better than random guessing at predicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations (DOE) score, from the same underlying data. Our measure is an order of magnitude better than the capability ratio at predicting dispute outcomes. We replicate Reed et al. (2008) and find, contrary to the original conclusions, that the probability of conflict is always highest when the state with the least benefits has a preponderance of power. In replications of 18 other dyadic analyses that use power as a control, we find that replacing the standard measure with DOE scores usually improves both in‐sample and out‐of‐sample goodness of fit.