2022-06-28-bahadori22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation	Proceedings of the 39th International Conference on Machine Learning	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bahadori22a	0	End-to-End Balancing for Causal Continuous Treatment-Effect Estimation	1313	1326	1313-1326	1313	false	Bahadori, Taha and Tchetgen, Eric Tchetgen and Heckerman, David	given family Taha Bahadori given family Eric Tchetgen Tchetgen given family David Heckerman	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bahadori22a/bahadori22a.pdf