2021/03/14 by Lizhen Nie, Nie, Lizhen, Mao Ye +6 · 17 citations
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Algorithm #Artificial intelligence #Artificial neural network #Block (permutation group theory) #Computer science #Estimator #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Observational study #Parametric model #Parametric statistics #Regularization (linguistics) #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2103.07861
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/03/14 · openalex publication_date 2021/03/14 · arxiv updated 2021/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.