Confounders in deep learning are in general detrimental to model's
generalization where they infiltrate feature representations. Therefore,
learning causal features that are free of interference from confounders is
important. Most previous causal learning based approaches employ back-door
criterion to mitigate the adverse effect of certain specific confounder, which
require the explicit identification of confounder. However, in real scenarios,
confounders are typically diverse and difficult to be identified. In this
paper, we propose a novel Confounder Identification-free Causal Visual Feature
Learning (CICF) method, which obviates the need for identifying confounders.
CICF models the interventions among different samples based on front-door
criterion, and then approximates the global-scope intervening effect upon the
instance-level interventions from the perspective of optimization. In this way,
we aim to find a reliable optimization direction, which avoids the intervening
effects of confounders, to learn causal features. Furthermore, we uncover the
relation between CICF and the popular meta-learning strategy MAML, and provide
an interpretation of why MAML works from the theoretical perspective of causal
learning for the first time. Thanks to the effective learning of causal
features, our CICF enables models to have superior generalization capability.
Extensive experiments on domain generalization benchmark datasets demonstrate
the effectiveness of our CICF, which achieves the state-of-the-art performance.

本文提出了一种新的不需要识别混淆因素的因果视觉特征学习方法（CICF），通过前门准则模拟样本之间的干预，并从优化的角度近似全局范围内的干预效果，以找到一个可靠的优化方向，从而避免混淆因素的干扰来学习因果特征，并且从因果学习的理论角度解释了元学习方法 MAML 的工作原理。实验结果表明，CICF 具有更好的泛化能力，在领域泛化基准数据集上实现了最先进的性能。

无混杂因素识别因果视觉特征学习

Confounder Identification-free Causal Visual Feature Learning

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。