Probabilities of Causation for Continuous and Vector Variables

Yuta Kawakami, Manabu Kuroki, Jin Tian
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:1901-1921, 2024.

Abstract

*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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-kawakami24a, title = {Probabilities of Causation for Continuous and Vector Variables}, author = {Kawakami, Yuta and Kuroki, Manabu and Tian, Jin}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {1901--1921}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/kawakami24a/kawakami24a.pdf}, url = {https://proceedings.mlr.press/v244/kawakami24a.html}, abstract = {*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.} }
Endnote
%0 Conference Paper %T Probabilities of Causation for Continuous and Vector Variables %A Yuta Kawakami %A Manabu Kuroki %A Jin Tian %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-kawakami24a %I PMLR %P 1901--1921 %U https://proceedings.mlr.press/v244/kawakami24a.html %V 244 %X *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.
APA
Kawakami, Y., Kuroki, M. & Tian, J.. (2024). Probabilities of Causation for Continuous and Vector Variables. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:1901-1921 Available from https://proceedings.mlr.press/v244/kawakami24a.html.

Related Material