Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range [0.000,1.000]$[0.000,1.000]$. To solve this problem, this paper proposes counterfactual-based prevented and preventable proportions. When both causal effects and observed probabilities are available, we show that the proposed measures are identifiable under the negative monotonicity assumption. Additionally, when the negative monotonicity assumption is violated, we formulate the bounds on the proposed measures. We also show that negative monotonicity together with exogeneity induces equivalence between the proposed measures and existing measures.

中文翻译：

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基于反事实的预防和可预防的比例

预防和可预防分数已广泛用于医学科学，以评估可通过保护性暴露避免的新疾病的比例。但是，在实际情况中使用的大多数现有公式不能解释为没有任何进一步假设的比例，因为它们是根据不同的目标人群得出的，可能落在[0.000,1.000]$[0.000,1.000]$范围之外。为了解决这个问题，本文提出了基于反事实的预防和可预防比例。当因果效应和观察到的概率都可用时，我们表明在负单调性假设下，所提出的措施是可识别的。此外，当违反负单调性假设时，我们制定了建议措施的界限。