  In real-world machine learning systems, labels are often derived from user
behaviors that the system wishes to encourage. Over time, new models must be
trained as new training examples and features become available. However,
feedback loops between users and models can bias future user behavior, inducing
a presentation bias in the labels that compromises the ability to train new
models. In this paper, we propose counterfactual augmentation, a novel causal
method for correcting presentation bias using generated counterfactual labels.
Our empirical evaluations demonstrate that counterfactual augmentation yields
better downstream performance compared to both uncorrected models and existing
bias-correction methods. Model analyses further indicate that the generated
counterfactuals align closely with true counterfactuals in an oracle setting.
