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

Lizhen Nie, Mao Ye, qiang liu, Dan Nicolae

Keywords: causal inference, continuous treatment effect, doubly robustness

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.

One-sentence Summary: We propose a varying coefficient network and a functional targeted regularization for estimating continuous treatment.

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Data: [IHDP](https://paperswithcode.com/dataset/ihdp)