Spurious correlations threaten the validity of statistical classifiers. While
model accuracy may appear high when the test data is from the same distribution
as the training data, it can quickly degrade when the test distribution
changes. For example, it has been shown that classifiers perform poorly when
humans make minor modifications to change the label of an example. One solution
to increase model reliability and generalizability is to identify causal
associations between features and classes. In this paper, we propose to train a
robust text classifier by augmenting the training data with automatically
generated counterfactual data. We first identify likely causal features using a
statistical matching approach. Next, we generate counterfactual samples for the
original training data by substituting causal features with their antonyms and
then assigning opposite labels to the counterfactual samples. Finally, we
combine the original data and counterfactual data to train a robust classifier.
Experiments on two classification tasks show that a traditional classifier
trained on the original data does very poorly on human-generated counterfactual
samples (e.g., 10%-37% drop in accuracy). However, the classifier trained on
the combined data is more robust and performs well on both the original test
data and the counterfactual test data (e.g., 12%-25% increase in accuracy
compared with the traditional classifier). Detailed analysis shows that the
robust classifier makes meaningful and trustworthy predictions by emphasizing
causal features and de-emphasizing non-causal features.

通过增加具有相反特性和标签的反事实数据来训练鲁棒的文本分类器，以强化模型可靠性和泛化性，实验证明，其在人工生成对照样本和原始测试数据上表现出较高的鲁棒性和精准度。

通过自动生成的反事实情况实现文本分类中对伪相关性的鲁棒性

Robustness to Spurious Correlations in Text Classification via  Automatically Generated Counterfactuals

Learning disentanglement aims at finding a low dimensional representation
which consists of multiple explanatory and generative factors of the
observational data. The framework of variational autoencoder (VAE) is commonly
used to disentangle independent factors from observations. However, in real
scenarios, factors with semantics are not necessarily independent. Instead,
there might be an underlying causal structure which renders these factors
dependent. We thus propose a new VAE based framework named CausalVAE, which
includes a Causal Layer to transform independent exogenous factors into causal
endogenous ones that correspond to causally related concepts in data. We
further analyze the model identifiabitily, showing that the proposed model
learned from observations recovers the true one up to a certain degree.
Experiments are conducted on various datasets, including synthetic and real
word benchmark CelebA. Results show that the causal representations learned by
CausalVAE are semantically interpretable, and their causal relationship as a
Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we
demonstrate that the proposed CausalVAE model is able to generate
counterfactual data through "do-operation" to the causal factors.

本文提出了一种基于因果结构的 VAE 算法 CausalVAE，使用因果层将独立外生因素转化为内生因果因素，实现了因果因素的自动发掘和可解释性，实验结果表明 CausalVAE 提出的因果表示能够在多个数据集上实现对真实因果结构的恢复，同时可以生成对因果结构产生干预的对事实数据。