  Utilizing covariate information has been a powerful approach to improve the
efficiency and accuracy for causal inference, which support massive amount of
randomized experiments run on data-driven enterprises. However, state-of-art
approaches can become practically unreliable when the dimension of covariate
increases to just 50, whereas experiments on large platforms can observe even
higher dimension of covariate. We propose a machine-learning-assisted covariate
representation approach that can effectively make use of historical experiment
or observational data that are run on the same platform to understand which
lower dimensions can effectively represent the higher-dimensional covariate. We
then propose design and estimation methods with the covariate representation.
We prove statistically reliability and performance guarantees for the proposed
methods. The empirical performance is demonstrated using numerical experiments.
