  Within the field of causal inference, we consider the problem of estimating
heterogeneous treatment effects from data. We propose and validate a novel
approach for learning feature representations to aid the estimation of the
conditional average treatment effect or CATE. Our method focuses on an
intermediate layer in a neural network trained to predict the outcome from the
features. In contrast to previous approaches that encourage the distribution of
representations to be treatment-invariant, we leverage a genetic algorithm that
optimizes over representations useful for predicting the outcome to select
those less useful for predicting the treatment. This allows us to retain
information within the features useful for predicting outcome even if that
information may be related to treatment assignment. We validate our method on
synthetic examples and illustrate its use on a real life dataset.

Machine Learning;Machine Learning