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Graphical identifiability criteria for causal effects in studies with an unobserved treatment/response variable

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  • Manabu Kuroki

Abstract

We consider the problem of using data in studies with an unobserved treatment/response variable in order to evaluate average causal effects, when cause-effect relationships between variables can be described by a directed acyclic graph and the corresponding recursive factorization of a joint distribution. The paper proposes graphical criteria to test whether average causal effects are identifiable even if a treatment/response variable is unobserved. If the answer is affirmative, we provide further formulations for average causal effects from the observed data. The graphical criteria enable us to evaluate average causal effects when it is difficult to observe a treatment/response variable. Copyright 2007, Oxford University Press.

Suggested Citation

  • Manabu Kuroki, 2007. "Graphical identifiability criteria for causal effects in studies with an unobserved treatment/response variable," Biometrika, Biometrika Trust, vol. 94(1), pages 37-47.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:1:p:37-47
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    File URL: http://hdl.handle.net/10.1093/biomet/asm005
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    Cited by:

    1. Elena Stanghellini & Eduwin Pakpahan, 2015. "Identification of causal effects in linear models: beyond instrumental variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 489-509, September.
    2. Manabu Kuroki & Takahiro Hayashi, 2016. "On the Estimation Accuracy of Causal Effects using Supplementary Variables," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 505-519, June.
    3. Manabu Kuroki, 2016. "The Identification of Direct and Indirect Effects in Studies with an Unmeasured Intermediate Variable," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 228-245, March.

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