Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
This work addresses causal inference for researchers and practitioners, but it is incremental as it adapts an existing method to neural networks.
The authors tackled the problem of estimating heterogeneous treatment effects by developing a fully connected neural network implementation of the Bayesian Causal Forest algorithm, showing performance improvements in simulations and applying it to a dataset on stress and sleep effects.
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.