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

TL;DR

This paper introduces a neural network architecture for causal inference, specifically implementing a Bayesian Causal Forest, and demonstrates its improved performance over existing methods through simulations and real data application.

Contribution

It develops a neural network version of the Bayesian Causal Forest for estimating heterogeneous treatment effects, advancing causal inference methods with neural networks.

Findings

Neural network implementation outperforms existing methods in simulations.

The approach effectively estimates treatment effects in real-world data.

Demonstrates the potential of neural networks in causal inference tasks.

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 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.