  In real world systems, the predictions of deployed Machine Learned models
affect the training data available to build subsequent models. This introduces
a bias in the training data that needs to be addressed. Existing solutions to
this problem attempt to resolve the problem by either casting this in the
reinforcement learning framework or by quantifying the bias and re-weighting
the loss functions. In this work, we develop a novel Adversarial Neural Network
(ANN) model, an alternative approach which creates a representation of the data
that is invariant to the bias. We take the Paid Search auction as our working
example and ad display position features as the confounding features for this
setting. We show the success of this approach empirically on both synthetic
data as well as real world paid search auction data from a major search engine.
