In causal inference, it is a fundamental task to estimate the causal effect
from observational data. However, latent confounders pose major challenges in
causal inference in observational data, for example, confounding bias and
M-bias. Recent data-driven causal effect estimators tackle the confounding bias
problem via balanced representation learning, but assume no M-bias in the
system, thus they fail to handle the M-bias. In this paper, we identify a
challenging and unsolved problem caused by a variable that leads to confounding
bias and M-bias simultaneously. To address this problem with co-occurring
M-bias and confounding bias, we propose a novel Disentangled Latent
Representation learning framework for learning latent representations from
proxy variables for unbiased Causal effect Estimation (DLRCE) from
observational data. Specifically, DLRCE learns three sets of latent
representations from the measured proxy variables to adjust for the confounding
bias and M-bias. Extensive experiments on both synthetic and three real-world
datasets demonstrate that DLRCE significantly outperforms the state-of-the-art
estimators in the case of the presence of both confounding bias and M-bias.

通过从代理变量学习潜在表示来调整混淆偏差和 M 偏差，我们提出了一种用于无偏因果效应估计（DLRCE）的新型解缠的潜在表示学习框架。实验证明 DLRCE 在同时存在混淆偏差和 M 偏差的情况下显著优于最先进的估计器。