Identification and Estimation of “Causes of Effects” using Covariate-Mediator Information

Ryusei Shingaki, Manabu Kuroki
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:3574-3582, 2024.

Abstract

In this paper, we deal with the evaluation problem of "causes of effects" (CoE), which focuses on the likelihood that one event was the cause of another. To assess this likelihood, three types of probabilities of causation have been utilized: probability of necessity, probability of sufficiency, and probability of necessity and sufficiency. However, these usually cannot be estimated, even if "effects of causes" (EoC) is estimable from statistical data, regardless of how large the data is. To solve this problem, we propose novel identification conditions for CoE, using an intermediate variable together with covariate information. Additionally, we also propose a new method for estimating CoE that is applicable whenever they are identifiable through the proposed identification conditions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-shingaki24a, title = { Identification and Estimation of “Causes of Effects” using Covariate-Mediator Information }, author = {Shingaki, Ryusei and Kuroki, Manabu}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {3574--3582}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/shingaki24a/shingaki24a.pdf}, url = {https://proceedings.mlr.press/v238/shingaki24a.html}, abstract = { In this paper, we deal with the evaluation problem of "causes of effects" (CoE), which focuses on the likelihood that one event was the cause of another. To assess this likelihood, three types of probabilities of causation have been utilized: probability of necessity, probability of sufficiency, and probability of necessity and sufficiency. However, these usually cannot be estimated, even if "effects of causes" (EoC) is estimable from statistical data, regardless of how large the data is. To solve this problem, we propose novel identification conditions for CoE, using an intermediate variable together with covariate information. Additionally, we also propose a new method for estimating CoE that is applicable whenever they are identifiable through the proposed identification conditions. } }
Endnote
%0 Conference Paper %T Identification and Estimation of “Causes of Effects” using Covariate-Mediator Information %A Ryusei Shingaki %A Manabu Kuroki %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-shingaki24a %I PMLR %P 3574--3582 %U https://proceedings.mlr.press/v238/shingaki24a.html %V 238 %X In this paper, we deal with the evaluation problem of "causes of effects" (CoE), which focuses on the likelihood that one event was the cause of another. To assess this likelihood, three types of probabilities of causation have been utilized: probability of necessity, probability of sufficiency, and probability of necessity and sufficiency. However, these usually cannot be estimated, even if "effects of causes" (EoC) is estimable from statistical data, regardless of how large the data is. To solve this problem, we propose novel identification conditions for CoE, using an intermediate variable together with covariate information. Additionally, we also propose a new method for estimating CoE that is applicable whenever they are identifiable through the proposed identification conditions.
APA
Shingaki, R. & Kuroki, M.. (2024). Identification and Estimation of “Causes of Effects” using Covariate-Mediator Information . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:3574-3582 Available from https://proceedings.mlr.press/v238/shingaki24a.html.

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