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Identification of casual effects in linear models: beyond Instrumental Variables


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  • Elena Stanghellini
  • Eduwin Pakpahan
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    The Instrumental Variable (IV) formula has become widely used to address the issue of identification of a causal effect in linear systems with an unobserved variable that acts as direct confounder. We here propose two alternative formulations to achieve identification when the assumptions underlying the use of IV are violated. Parallel to the IV, the proposed formulas exploit the conditional independence structure of a Directed Acyclic Graph and can be obtained via a series of univariate regressions, a feature that renders the results particularly attractive and easy to implement.

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    Bibliographic Info

    Paper provided by Università di Perugia, Dipartimento Economia, Finanza e Statistica in its series Quaderni del Dipartimento di Economia, Finanza e Statistica with number 117/2013.

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    Length: 28 pages
    Date of creation: 02 May 2013
    Date of revision:
    Handle: RePEc:pia:wpaper:117/2013

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    Keywords: Causal Effect; Confounder; Directed Acyclic Graph; Latent Variable; Regression Graph; Structural Equation Model; Identification; Structural Equation Model;

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    1. Nanny Wermuth & D. R. Cox, 2008. "Distortion of effects caused by indirect confounding," Biometrika, Biometrika Trust, vol. 95(1), pages 17-33.
    2. Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 12(2), pages 281-345, December.
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