Latent confounders---unobserved variables that influence both treatment and
outcome---can bias estimates of causal effects. In some cases, these
confounders are shared across observations, e.g. all students taking a course
are influenced by the course's difficulty in addition to any educational
interventions they receive individually. This paper shows how to
semiparametrically model latent confounders that have this structure and
thereby improve estimates of causal effects. The key innovations are a
hierarchical Bayesian model, Gaussian processes with structured latent
confounders (GP-SLC), and a Monte Carlo inference algorithm for this model
based on elliptical slice sampling. GP-SLC provides principled Bayesian
uncertainty estimates of individual treatment effect with minimal assumptions
about the functional forms relating confounders, covariates, treatment, and
outcome. Finally, this paper shows GP-SLC is competitive with or more accurate
than widely used causal inference techniques on three benchmark datasets,
including the Infant Health and Development Program and a dataset showing the
effect of changing temperatures on state-wide energy consumption across New
England.

本文介绍了如何半参数地建模具有结构化潜在混淆因素以提高因果效应估计并给出一个基于椭圆切片取样的贝叶斯推理算法；最后展示了基于这个模型的 GP-SLC 在包括婴儿健康与发展项目和新英格兰州全州能耗变化数据集等三个基准数据集上与广泛使用的因果推断技术相比具有竞争力或更高的准确性。