  Double machine learning is a statistical method for leveraging complex
black-box models to construct approximately unbiased treatment effect estimates
given observational data with high-dimensional covariates, under the assumption
of a partially linear model. The idea is to first fit on a subset of the
samples two non-linear predictive models, one for the continuous outcome of
interest and one for the observed treatment, and then to estimate a linear
coefficient for the treatment using the remaining samples through a simple
orthogonalized regression. While this methodology is flexible and can
accommodate arbitrary predictive models, typically trained independently of one
another, this paper argues that a carefully coordinated learning algorithm for
deep neural networks may reduce the estimation bias. The improved empirical
performance of the proposed method is demonstrated through numerical
experiments on both simulated and real data.
