Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between. Concurrently, the study and usage of neural networks has also grown profoundly (albeit at a far faster rate). What we aim to do in this blog write-up is demonstrate a Neural Network causal inference architecture. We develop a fully connected neural network implementation of the popular Bayesian Causal Forest algorithm, a state of the art tree based method for estimating heterogeneous treatment effects. We compare our implementation to existing neural network causal inference methodologies, showing improvements in performance in simulation settings. We apply our method to a dataset examining the effect of stress on sleep.

翻译：近年来，因果推断在学术界、工业界、教育界等各个领域广受关注。与此同时，神经网络的研究与应用也取得了长足发展（尽管增速更为迅猛）。本博客旨在展示一种神经网络因果推断架构。我们基于流行的贝叶斯因果森林算法——一种用于估计异质性处理效应的先进树模型方法——开发了全连接神经网络实现。通过与现有神经网络因果推断方法进行对比，我们证明了该实现方法在模拟场景中的性能提升。此外，我们将所提方法应用于探究压力对睡眠影响的数据集。