Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimation
<|reference_start|>Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimation: Continuous treatment effect estimation holds significant practical importance across various decision-making and assessment domains, such as healthcare and the military. However, current methods for estimating dose-response curves hinge on balancing the entire representation by treating all covariates as confounding variables. Although various approaches disentangle covariates into different factors for treatment effect estimation, they are confined to binary treatment settings. Moreover, observational data are often tainted with non-causal noise information that is imperceptible to the human. Hence, in this paper, we propose a novel Dose-Response curve estimator via Variational AutoEncoder (DRVAE) disentangled covariates representation. Our model is dedicated to disentangling covariates into instrumental factors, confounding factors, adjustment factors, and external noise factors, thereby facilitating the estimation of treatment effects under continuous treatment settings by balancing the disentangled confounding factors. Extensive results on synthetic and semi-synthetic datasets demonstrate that our model outperforms the current state-of-the-art methods.<|reference_end|>
@article{cui2024disentangled,
    title={Disentangled Representation via Variational AutoEncoder for Continuous
  Treatment Effect Estimation},
    author={Ruijing Cui, Jianbin Sun, Bingyu He, Kewei Yang, Bingfeng Ge},
    journal={arXiv preprint arXiv:2406.02310},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02310},
    primaryClass={cs.LG}
}