2024-09-12-kawakami24b.md

title	abstract	openreview	section	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
Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable	There has been considerable recent interest in estimating heterogeneous causal effects. In this paper, we study conditional average partial causal effects (CAPCE) to reveal the heterogeneity of causal effects with continuous treatment. We provide conditions for identifying CAPCE in an instrumental variable setting. Notably, CAPCE is identifiable under a weaker assumption than required by a commonly used measure for estimating heterogeneous causal effects of continuous treatment. We develop three families of CAPCE estimators: sieve, parametric, and reproducing kernel Hilbert space (RKHS)-based, and analyze their statistical properties. We illustrate the proposed CAPCE estimators on synthetic and real-world data.	StHUKqZGNs	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kawakami24b	0	Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable	1922	1952	1922-1952	1922	false	Kawakami, Yuta and Kuroki, Manabu and Tian, Jin	given family Yuta Kawakami given family Manabu Kuroki given family Jin Tian	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/kawakami24b/kawakami24b.pdf