Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available parametric methods are limited in their model space, and previous attempts in leveraging neural network to enhance model expressiveness relied on partitioning continuous treatment into blocks and using separate heads for each block; this however produces in practice discontinuous ADRFs. Therefore, the question of how to adapt the structure and training of neural network to estimate ADRFs remains open. This paper makes two important contributions. First, we propose a novel varying coefficient neural network (VCNet) that improves model expressiveness while preserving continuity of the estimated ADRF. Second, to improve finite sample performance, we generalize targeted regularization to obtain a doubly robust estimator of the whole ADRF curve.
翻译:基于不断治疗的观测数据越来越多,我们调查了估算平均剂量反应曲线(ADRF)的问题。现有参数方法在模型空间方面有限,而且以前曾试图利用神经网络来提高模型的直观性,而模型的直观性则依赖于将连续处理分成区块和对每个区块使用单独的头;然而,这实际上产生了不连续的ADRFs。因此,如何调整神经网络的结构和培训以估计ADRFs的结构和培训问题仍然悬而未决。本文件作出了两个重要贡献。首先,我们提议建立一个新的不同系数神经网络(VCNet),改进模型的直观性,同时保持估计的ADRF的连续性。第二,为了改进有限的样本性能,我们普遍地将目标正规化化作为全ADRF曲线的双重强势估计。