论文标题
在选择偏见下的反事实界限
Bounding Counterfactuals under Selection Bias
论文作者
论文摘要
因果分析可能会受到选择偏差的影响，该偏差定义为从某个亚群中系统排除数据。该领域的先前工作集中在可识别性条件的推导上。我们建议使用第一种算法来解决可识别和无法识别的查询。我们证明，尽管选择偏差引起的缺失，但可用数据的可能性是单峰的。这使我们能够使用因果期望最大化方案来获得可识别的情况下的因果查询值，并否则可以计算界限。实验证明了实际上可行的方法。提供了理论收敛特征。
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.
