Dorie Vincent, Perrett George, Hill Jennifer L, Goodrich Benjamin
Code for America, San Francisco, CA 94103, USA.
Department of Applied Statistics, Social Science, and the Humanities, New York University, New York, NY 10003, USA.
Entropy (Basel). 2022 Dec 6;24(12):1782. doi: 10.3390/e24121782.
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and heterogeneous treatment effects can be hampered when data are structured within groups if we fail to correctly model the dependence between observations. Most machine learning methods do not readily accommodate such structure. This paper introduces a new algorithm, stan4bart, that combines the flexibility of Bayesian Additive Regression Trees (BART) for fitting nonlinear response surfaces with the computational and statistical efficiencies of using Stan for the parametric components of the model. We demonstrate how stan4bart can be used to estimate average, subgroup, and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data as well as multilevel approaches that have strict parametric forms.
在过去十年中，人们开发了各种各样基于机器学习的方法，提高了我们对非线性和非加性响应面进行精确建模的能力。这提高了推理任务的性能，例如在标准参数模型可能无法很好拟合数据的情况下估计平均治疗效果。这些方法在识别异质治疗效果的相关任务中也显示出了前景。然而，如果我们未能正确建模观测值之间的依赖性，那么当数据按组结构化时，总体和异质治疗效果的估计可能会受到阻碍。大多数机器学习方法不容易适应这种结构。本文介绍了一种新算法stan4bart，它将贝叶斯加法回归树（BART）拟合非线性响应面的灵活性与使用Stan处理模型参数组件的计算和统计效率相结合。我们展示了stan4bart如何用于估计平均、亚组和个体水平的治疗效果，其性能优于其他忽略数据多层次结构的灵活方法以及具有严格参数形式的多层次方法。
