The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification

Manabu Kuroki, Zhihong Cai
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:333-340, 2008.

Abstract

This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/outcome variable in many practical fields.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-kuroki08a, title = {The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification}, author = {Kuroki, Manabu and Cai, Zhihong}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {333--340}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/kuroki08a/kuroki08a.pdf}, url = {https://proceedings.mlr.press/r6/kuroki08a.html}, abstract = {This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/outcome variable in many practical fields.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification %A Manabu Kuroki %A Zhihong Cai %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-kuroki08a %I PMLR %P 333--340 %U https://proceedings.mlr.press/r6/kuroki08a.html %V R6 %X This paper deals with the problem of evaluating the causal effect using observational data in the presence of an unobserved exposure/outcome variable, when cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding recursive factorization of a joint distribution. First, we propose identifiability criteria for causal effects when an unobserved exposure/outcome variable is considered to contain more than two categories. Next, when unmeasured variables exist between an unobserved outcome variable and its proxy variables, we provide the tightest bounds based on the potential outcome approach. The results of this paper are helpful to evaluate causal effects in the case where it is difficult or expensive to observe an exposure/outcome variable in many practical fields. %Z Reissued by PMLR on 09 October 2024.
APA
Kuroki, M. & Cai, Z.. (2008). The evaluation of causal effects in studies with an unobserved exposure/outcome variable: bounds and identification. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:333-340 Available from https://proceedings.mlr.press/r6/kuroki08a.html. Reissued by PMLR on 09 October 2024.

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