VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

2021/03/14 by Lizhen Nie, Nie, Lizhen, Mao Ye +5 · 4 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2103.07861

openalex publication_date 2021/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Local asymptotics for polynomial spline regression
• Density Estimation Using Real NVP
• Double/Debiased/Neyman Machine Learning of Treatment Effects
• The Kernel Mixture Network: A Nonparametric Method for Conditional\n Density Estimation of Continuous Random Variables
• Causal Effect Inference with Deep Latent-Variable Models
• Conditional Density Estimation with Bayesian Normalising Flows

Cited by

• Multi-Treatment-DML: Causal Estimation for Multi-Dimensional Continuous Treatments with Monotonicity Constraints in Personal Loan Risk Optimization
• Beyond the ATE: Interpretable Modelling of Treatment Effects over Dose and Time
• Hidden Representation Clustering with Multi-Task Representation Learning towards Robust Online Budget Allocation
• Projection Pursuit Density Ratio Estimation

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