论文标题
超越条件平均：估计单个因果效应分布
Beyond Conditional Averages: Estimating The Individual Causal Effect Distribution
论文作者
论文摘要
近年来，观察数据的因果推断领域已迅速出现。文献集中在（条件）平均因果效应估计上。当个人因果效应（ICES）的（剩余）变异性相当大时，平均效应可能对一个人来说可能是无信息的。因果推论的基本问题排除了估计潜在结果的联合分布而无需做出假设。在这项工作中，我们表明，除了一致性，积极性和条件交换性的共同假设外，冰分布在个人效应的（条件）独立性和无暴露下的潜在结果下都是可识别的。此外，我们提出了一个灵活的潜在变量模型家族，可用于研究单个效应修改并估算横截面数据的冰分布。在一项关于肝脂肪变性对临床前体对心力衰竭的影响的案例研究中，如何在实践中应用和验证这种潜在可变模型。根据提出的假设，我们估计20.6％（贝叶斯可信间隔95％：8.9％，33.6％）的有害作用大于平均因果关系的两倍。
In recent years, the field of causal inference from observational data has emerged rapidly. The literature has focused on (conditional) average causal effect estimation. When (remaining) variability of individual causal effects (ICEs) is considerable, average effects may be uninformative for an individual. The fundamental problem of causal inference precludes estimating the joint distribution of potential outcomes without making assumptions. In this work, we show that the ICE distribution is identifiable under (conditional) independence of the individual effect and the potential outcome under no exposure, in addition to the common assumptions of consistency, positivity, and conditional exchangeability. Moreover, we present a family of flexible latent variable models that can be used to study individual effect modification and estimate the ICE distribution from cross-sectional data. How such latent variable models can be applied and validated in practice is illustrated in a case study on the effect of Hepatic Steatosis on a clinical precursor to heart failure. Under the assumptions presented, we estimate that 20.6% (95% Bayesian credible interval: 8.9%, 33.6%) of the population has a harmful effect greater than twice the average causal effect.
