2024-09-12-kawakami24a.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
Probabilities of Causation for Continuous and Vector Variables	*Probabilities of causation* (PoC) are valuable concepts for explainable artificial intelligence and practical decision-making. PoC are originally defined for scalar binary variables. In this paper, we extend the concept of PoC to continuous treatment and outcome variables, and further generalize PoC to capture causal effects between multiple treatments and multiple outcomes. In addition, we consider PoC for a sub-population and PoC with multi-hypothetical terms to capture more sophisticated counterfactual information useful for decision-making. We provide a nonparametric identification theorem for each type of PoC we introduce. Finally, we illustrate the application of our results on a real-world dataset about education.	em3fzm95So	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kawakami24a	0	Probabilities of Causation for Continuous and Vector Variables	1901	1921	1901-1921	1901	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/kawakami24a/kawakami24a.pdf