论文标题
从隐藏变量的数据中迈出独特和公正的因果效应估计
Towards unique and unbiased causal effect estimation from data with hidden variables
论文作者
论文摘要
观察数据中的因果效应估计是一项至关重要但具有挑战性的任务。当前，仅可用数据驱动的因果效应估计方法有限。这些方法要么仅提供对治疗对结果的因果效应的约束估计，要么对因果效应产生独特的估计，但对数据进行了强有力的假设和低效率。在本文中，我们确定了一个实用的问题设定，并提出了一种方法，以从具有隐藏变量的数据中实现对因果效应的独特且无偏见的估计。对于方法，我们已经开发了定理，以支持发现适当的协变量集以进行混淆调整（调整集）。根据定理，提出了两种算法，以从具有隐藏变量的数据中找到适当的调整集，以获得无偏见且独特的因果效应估计。使用五个基准贝叶斯网络和四个现实世界数据集生成的合成数据集的实验证明了所提出的算法的效率和有效性，表明已确定的问题设置的实用性以及在现实世界应用中提出的方法的潜力。
Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of the causal effect of a treatment on the outcome, or generate a unique estimation of the causal effect, but making strong assumptions on data and having low efficiency. In this paper, we identify a practical problem setting and propose an approach to achieving unique and unbiased estimation of causal effects from data with hidden variables. For the approach, we have developed the theorems to support the discovery of the proper covariate sets for confounding adjustment (adjustment sets). Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation. Experiments with synthetic datasets generated using five benchmark Bayesian networks and four real-world datasets have demonstrated the efficiency and effectiveness of the proposed algorithms, indicating the practicability of the identified problem setting and the potential of the proposed approach in real-world applications.
