Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl's front-door (FD) criterion (Pearl, 1995). The definition of the FD criterion is declarative, only allowing one to decide whether a specific set satisfies the criterion. In this paper, we present algorithms for finding and enumerating possible sets satisfying the FD criterion in a given causal diagram. These results are useful in facilitating the practical applications of the FD criterion for causal effects estimation and helping scientists to select estimands with desired properties, e.g., based on cost, feasibility of measurement, or statistical power.

翻译：从数据中确定新干预措施的效果是广泛经验科学中发现的一项重大挑战,查明这种效果的一个众所周知的战略是珍珠的前门(FD)标准(Pearl,1995年);FD标准的定义是宣示性的,只允许一个人决定某一套具体指标是否符合标准;在本文件中,我们提出在某一因果图表中查找和列出符合FD标准的可能数据集的算法,这些结果有助于便利FD标准在因果关系估计方面的实际应用,并帮助科学家根据成本、计量的可行性或统计能力等标准选择具有预期特性的估算值。