Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

arXiv:2405.03130v1 Announce Type: cross
Abstract: 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 …

abstract academic architectures arxiv causal causal inference comparison cs.lg deep learning educational faster industrial inference networks neural networks rate stat.ml study treatment type usage