85 research outputs found
Rethinking Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mitigate
confounding biases in the training data for a machine learning model. These
biases, such as spurious correlations, arise due to various observed and
unobserved confounding variables in the data generation process. In this paper,
we formally analyze how confounding biases impact downstream classifiers and
present a causal viewpoint to the solutions based on counterfactual data
augmentation. We explore how removing confounding biases serves as a means to
learn invariant features, ultimately aiding in generalization beyond the
observed data distribution. Additionally, we present a straightforward yet
powerful algorithm for generating counterfactual images, which effectively
mitigates the influence of confounding effects on downstream classifiers.
Through experiments on MNIST variants and the CelebA datasets, we demonstrate
the effectiveness and practicality of our approach
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