Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimation

TL;DR

This paper introduces a novel variational autoencoder-based method for estimating continuous treatment effects by disentangling covariates into distinct factors, improving accuracy over existing approaches.

Contribution

It proposes a new disentangled covariate representation model specifically designed for continuous treatment effect estimation, addressing limitations of prior binary-focused methods.

Findings

Outperforms current state-of-the-art methods on synthetic datasets

Effectively disentangles covariates into multiple factors

Improves treatment effect estimation accuracy

Abstract

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.