  How should we evaluate the effect of a policy on the likelihood of an
undesirable event, such as conflict? The significance test has three
limitations. First, relying on statistical significance misses the fact that
uncertainty is a continuous scale. Second, focusing on a standard point
estimate overlooks the variation in plausible effect sizes. Third, the
criterion of substantive significance is rarely explained or justified. A new
Bayesian decision-theoretic model, "causal binary loss function model,"
overcomes these issues. It compares the expected loss under a policy
intervention with the one under no intervention. These losses are computed
based on a particular range of the effect sizes of a policy, the probability
mass of this effect size range, the cost of the policy, and the cost of the
undesirable event the policy intends to address. The model is more applicable
than common statistical decision-theoretic models using the standard loss
functions or capturing costs in terms of false positives and false negatives. I
exemplify the model's use through three applications and provide an R package.
