Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
<|reference_start|>Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation: 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.<|reference_end|>
@article{papakostas2024deep,
    title={Deep Learning for Causal Inference: A Comparison of Architectures for
  Heterogeneous Treatment Effect Estimation},
    author={Demetrios Papakostas, Andrew Herren, P. Richard Hahn, Francisco
  Castillo},
    journal={arXiv preprint arXiv:2405.03130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03130},
    primaryClass={stat.ML cs.LG}
}