How should we evaluate a policy's effect on the likelihood of an undesirable event, such as conflict? The conventional practice 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 a variation in plausible effect sizes. Third, the criterion of substantive significance is rarely explained or justified. To overcome these, my Bayesian decision-theoretic model compares the expected loss under a policy intervention with the one under no such intervention. These losses are computed as a function of a particular effect size, the probability of this effect being realized, and the ratio of the cost of an intervention to the cost of an undesirable event. The model is more practically interpretable than common statistical decision-theoretic models using the standard loss functions or the relative costs of false positives and false negatives. I exemplify my model's use through three applications and provide an R package.

翻译：我们应如何评价政策对冲突等不良事件可能性的影响?常规做法有三个限制。首先,依赖统计意义,忽略了不确定性是一个连续规模的事实。其次,侧重于标准点估计,忽略了合理效果大小的差异。第三,实质性意义标准很少得到解释或说明。为了克服这些,我的贝叶斯人决定理论模型将政策干预下的预期损失与没有此类干预的预期损失进行比较。这些损失是按特定影响大小、实现这种影响的可能性以及干预费用与不良事件费用之比计算的。这个模型比使用标准损失函数或假正数和假负数的相对成本的通用统计决定理论模型更具实际解释性。我举例说明我的模型通过三种应用的用途,并提供R组合。