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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.03130 |
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| _version_ | 1866917658071400448 |
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| author | Papakostas, Demetrios Herren, Andrew Hahn, P. Richard Castillo, Francisco |
| author_facet | Papakostas, Demetrios Herren, Andrew Hahn, P. Richard Castillo, Francisco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation Papakostas, Demetrios Herren, Andrew Hahn, P. Richard Castillo, Francisco Machine Learning 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. |
| title | Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.03130 |