The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process Analysis

Manabu Kuroki, Zhihong Cai
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, PMLR R4:179-186, 2003.

Abstract

Consider a case where cause-effect relationships among variables can be described by a causal diagram and the corresponding linear structural equation model. In order to bring a response variable close to a target, this paper proposes a statistical method for inferring a joint causal effect of a conditional plan on the variance of a response variable from nonexperimental data. Moreover, based on this method, this paper formulates a conditional plan, which can cancel the influence of covariates on a response variable. The results of this paper could enable us to select an effective plan in linear conditional plans.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR4-kuroki03a, title = {The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process Analysis}, author = {Kuroki, Manabu and Cai, Zhihong}, booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics}, pages = {179--186}, year = {2003}, editor = {Bishop, Christopher M. and Frey, Brendan J.}, volume = {R4}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r4/kuroki03a/kuroki03a.pdf}, url = {https://proceedings.mlr.press/r4/kuroki03a.html}, abstract = {Consider a case where cause-effect relationships among variables can be described by a causal diagram and the corresponding linear structural equation model. In order to bring a response variable close to a target, this paper proposes a statistical method for inferring a joint causal effect of a conditional plan on the variance of a response variable from nonexperimental data. Moreover, based on this method, this paper formulates a conditional plan, which can cancel the influence of covariates on a response variable. The results of this paper could enable us to select an effective plan in linear conditional plans.}, note = {Reissued by PMLR on 01 April 2021.} }
Endnote
%0 Conference Paper %T The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process Analysis %A Manabu Kuroki %A Zhihong Cai %B Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2003 %E Christopher M. Bishop %E Brendan J. Frey %F pmlr-vR4-kuroki03a %I PMLR %P 179--186 %U https://proceedings.mlr.press/r4/kuroki03a.html %V R4 %X Consider a case where cause-effect relationships among variables can be described by a causal diagram and the corresponding linear structural equation model. In order to bring a response variable close to a target, this paper proposes a statistical method for inferring a joint causal effect of a conditional plan on the variance of a response variable from nonexperimental data. Moreover, based on this method, this paper formulates a conditional plan, which can cancel the influence of covariates on a response variable. The results of this paper could enable us to select an effective plan in linear conditional plans. %Z Reissued by PMLR on 01 April 2021.
APA
Kuroki, M. & Cai, Z.. (2003). The Joint Causal Effect in Linear Structural Equation Model and Its Application to Process Analysis. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R4:179-186 Available from https://proceedings.mlr.press/r4/kuroki03a.html. Reissued by PMLR on 01 April 2021.

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