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We propose a novel approach for constructing effective treatment policies when the observed data is biased and lacks counterfactual information. Learning in settings where the observed data does not contain all possible outcomes for all treatments is difficult since the observed data is typically biased due to existing clinical guidelines. This is an important problem in the medical domain as collecting unbiased data is expensive and so learning from the wealth of existing biased data is a worthwhile task. Our approach separates the problem into two stages: first we reduce the bias by learning a representation map using a novel auto-encoder network---this allows us to control the trade-off between the bias-reduction and the information loss---and then we construct effective treatment policies on the transformed data using a novel feedforward network. Separation of the problem into these two stages creates an algorithm that can be adapted to the problem at hand---the bias-reduction step can be performed as a preprocessing step for other algorithms. We compare our algorithm against state-of-art algorithms on two semi-synthetic datasets and demonstrate that our algorithm achieves a significant improvement in performance.