DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation

Abstract

Estimating the individual-level continuous treatment effect holds significant practical importance in various decision-making domains, such as personalized healthcare and customized marketing. However, most current methods for individual treatment effect estimation are limited to discrete treatments and struggle to precisely adjust for selection bias under continuous settings, leading to inaccurate estimation. To address these challenges, we propose a novel Disentangled Representation Network (DTRNet) to estimate the individualized dose-response function (IDRF), which learns disentangled representations and precisely adjusts for selection bias. To the best of our knowledge, our work is the first attempt to precisely adjust for selection bias in continuous settings. Extensive results on synthetic and semi-synthetic datasets demonstrate that our DTRNet outperforms most state-of-the-art methods. Our code is available at \href{https://github.com/xuanxuan03021/DTRNet_final_2}DTRNet.