  Estimating heterogeneous treatment effect is an important task in causal
inference with wide application fields. It has also attracted increasing
attention from machine learning community in recent years. In this work, we
reinterpret the heterogeneous treatment effect estimation and propose ways to
borrow strength from neural networks. We analyze the strengths and drawbacks of
integrating neural networks into heterogeneous treatment effect estimation and
clarify the aspects that need to be taken into consideration when designing a
specific network. We proposed a specific network under our guidelines. In
simulations, we show that our network performs better when the structure of
data is complex, and reach a draw under the cases where other methods could be
proved to be optimal.
