Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking. In this work we present a scalable quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models. Contrary to Bayesian modeling for IV, our approach does not require additional assumptions on the data generating process, and leads to a scalable approximate inference algorithm with time cost comparable to the corresponding point estimation methods. Our algorithm can be further extended to work with neural network models. We analyze the theoretical properties of the proposed quasi-posterior, and demonstrate through empirical evaluation the competitive performance of our method.
翻译:近年来,人们对采用灵活的机器学习模型进行工具变量(四)回归的兴趣激增,但不确定量化方法的制定仍然缺乏。在这项工作中,我们提出了一种可扩展的准巴伊西亚程序,用于四级回归,以最近开发的内分泌四级模型为基础。与巴耶斯四级模型相反,我们的方法并不要求就数据生成过程作出额外假设,而是导致一种可扩展的近似推算算法,其时间成本可与相应的点估计方法相比。我们的算法可以进一步扩展至与神经网络模型一起工作。我们分析了拟议的准前方模型的理论属性,并通过经验评估展示了我们方法的竞争性性能。