Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. G-computation methods are often used in these scenarios, with several recent proposals using Bayesian versions of g-computation. In settings with few confounders, standard models can be employed, however as the number of confounders increase these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of MCMC methods. Approximations using the central limit theorem and random sampling allows our method to be scaled to high dimensional confounders while maintaining computational efficiency. We illustrate the model using carefully constructed simulation studies, as well as compare the computational costs to other benchmark models.

翻译：在观测研究中进行因果关系推断要求我们假设对混杂变量进行正确调整。 G-计算方法经常在这些假设中使用,最近还提出若干建议,使用巴伊西亚版本的g-compectation。在很少有混淆者的情况下,可以使用标准模型,然而,由于混淆者人数的增加,这些模型变得不那么可行,因为对于各种混杂变量的独特组合,现有观测数据较少。在本文件中,我们提出了一个新的模型,用于估算观测研究的处理效果,其中包括参数和非参数结果模型。通过在概念上将数据分开,我们可以将这些模型结合起来,同时保持一个共和框架,使我们能够避免使用MCMCM方法。使用中央限制的参数和随机抽样,使我们的方法在保持计算效率的同时,可以缩到高维度的共集体。我们用精心构建的模拟研究来说明模型,并将计算成本与其他基准模型进行比较。