  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.
