End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
Abstract
We study the problem of observational causal inference with continuous treatment. We focus on the challenge of estimating the causal response curve for infrequently-observed treatment values. We design a new algorithm based on the framework of entropy balancing which learns weights that directly maximize causal inference accuracy using end-to-end optimization. Our weights can be customized for different datasets and causal inference algorithms. We propose a new theory for consistency of entropy balancing for continuous treatments. Using synthetic and real-world data, we show that our proposed algorithm outperforms the entropy balancing in terms of causal inference accuracy.
Cite
Text
Bahadori et al. "End-to-End Balancing for Causal Continuous Treatment-Effect Estimation." International Conference on Machine Learning, 2022.Markdown
[Bahadori et al. "End-to-End Balancing for Causal Continuous Treatment-Effect Estimation." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/bahadori2022icml-endtoend/)BibTeX
@inproceedings{bahadori2022icml-endtoend,
title = {{End-to-End Balancing for Causal Continuous Treatment-Effect Estimation}},
author = {Bahadori, Taha and Tchetgen, Eric Tchetgen and Heckerman, David},
booktitle = {International Conference on Machine Learning},
year = {2022},
pages = {1313-1326},
volume = {162},
url = {https://mlanthology.org/icml/2022/bahadori2022icml-endtoend/}
}