  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.
