The fundamental problem in treatment effect estimation from observational
data is confounder identification and balancing. Most of the previous methods
realized confounder balancing by treating all observed pre-treatment variables
as confounders, ignoring further identifying confounders and non-confounders.
In general, not all the observed pre-treatment variables are confounders that
refer to the common causes of the treatment and the outcome, some variables
only contribute to the treatment and some only contribute to the outcome.
Balancing those non-confounders, including instrumental variables and
adjustment variables, would generate additional bias for treatment effect
estimation. By modeling the different causal relations among observed
pre-treatment variables, treatment and outcome, we propose a synergistic
learning framework to 1) identify confounders by learning decomposed
representations of both confounders and non-confounders, 2) balance confounder
with sample re-weighting technique, and simultaneously 3) estimate the
treatment effect in observational studies via counterfactual inference.
Empirical results on synthetic and real-world datasets demonstrate that the
proposed method can precisely decompose confounders and achieve a more precise
estimation of treatment effect than baselines.

该研究提出了一种基于 “协同学习” 框架的方法来识别混淆变量并通过样本重新加权技术与反事实推理方法同时准确估计处理效应。