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.

本文提出了一种利用变分自编码器（DRVAE）实现剂量响应曲线估计的新方法，通过将协变量分解为仪器因子、混淆因子、调整因子和外部噪声因子，从而在连续治疗情景下平衡分解的混淆因子，以实现治疗效果的估计。大量对合成和半合成数据集的实验结果证明，我们的模型优于当前的最先进方法。

基于变分自编码器的连续处理效应估计的解耦表示

Disentangled Representation via Variational AutoEncoder for Continuous  Treatment Effect Estimation

Detecting latent confounders from proxy variables is an essential problem in
causal effect estimation. Previous approaches are limited to low-dimensional
proxies, sorted proxies, and binary treatments. We remove these assumptions and
present a novel Proxy Confounder Factorization (PCF) framework for continuous
treatment effect estimation when latent confounders manifest through
high-dimensional, mixed proxy variables. For specific sample sizes, our
two-step PCF implementation, using Independent Component Analysis (ICA-PCF),
and the end-to-end implementation, using Gradient Descent (GD-PCF), achieve
high correlation with the latent confounder and low absolute error in causal
effect estimation with synthetic datasets in the high sample size regime. Even
when faced with climate data, ICA-PCF recovers four components that explain
$75.9\%$ of the variance in the North Atlantic Oscillation, a known confounder
of precipitation patterns in Europe. Code for our PCF implementations and
experiments can be found here: this https URL The
proposed methodology constitutes a stepping stone towards discovering latent
confounders and can be applied to many problems in disciplines dealing with
high-dimensional observed proxies, e.g., spatiotemporal fields.

从代理变量中检测潜在混淆因素是因果效应估计中的一个重要问题。我们提出了一种新颖的代理混淆因子分解（PCF）框架，用于处理通过高维、混合型代理变量表现出来的连续处理效应估计。