We consider the problem of assessing whether, in an individual case, there is a causal relationship between an observed exposure and a response variable. When data are available on similar individuals we may be able to estimate prospective probabilities, but even under ideal conditions these are typically inadequate to identify the "probability of causation": instead we can only derive bounds for this. These bounds can be improved or amended when we have information on additional variables, such as mediators or covariates. When a covariate is unobserved or ignored, this will typically lead to biased inferences. We show by examples how serious such biases can be.

翻译：我们考虑在个体案例中评估观测到的暴露与响应变量之间是否存在因果关系的问题。当可获得相似个体的数据时，我们或许能够估计前瞻性概率，但即使在理想条件下，这些概率通常也不足以识别"因果概率"：相反，我们只能推导出该概率的界。当拥有额外变量（如中介变量或协变量）的信息时，这些界可以得到改进或修正。当某个协变量未被观测或被忽略时，这通常会导致有偏推断。我们通过示例展示了这种偏差可能达到的严重程度。