论文标题
统计估计的政策影响：二进制结果的一般贝叶斯决策理论模型
Policy Implications of Statistical Estimates: A General Bayesian Decision-Theoretic Model for Binary Outcomes
论文作者
论文摘要
我们应该如何评估政策对不良事件（例如冲突）可能性的影响？显着性测试具有三个局限性。首先，依靠统计意义错过了不确定性是连续规模的事实。其次，专注于标准点估计值可忽视合理效应大小的变化。第三，很少解释或合理地说明实质性意义的标准。一个新的贝叶斯决策理论模型“因果二进制损失函数模型”克服了这些问题。它将政策干预下的预期损失与不干预的预期损失进行了比较。这些损失是根据政策效应大小的特定范围，此效果大小范围的概率质量，政策成本以及政策打算解决的不良事件的成本进行计算的。该模型比使用标准损失功能或以假阳性和假否定性来捕获成本的常见统计决策理论模型更适合。我通过三个应用程序说明了该模型的使用，并提供了R包。
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.
