Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the data sources available. This paper shows how partial identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted to within some narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded when such allowances are made. Often those allowances are easily measured and reasonably assumed. Finally, epsilon-identifiability is applied to the unit selection problem.
翻译:在几乎所有科学领域,确定影响的原因和原因都至关重要,然而,往往无法从现有数据来源中完全确定所需的概率,本文表明,对于若干因果关系可能性,部分可识别性还是可能的。我们使用这一百分百可识别性,并表明在某些亚人口的行为可限于某些狭义范围内的情况下,这种可识别性是有用的。特别是,我们表明,在作出这种允许时,如何对无法识别的因果关系和反事实概率进行狭义的限定,这些可识别性往往易于衡量和合理假定。最后,对单位选择问题适用了百分百可识别性。