Identification of casual effects in linear models: beyond Instrumental Variables
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.
|Date of creation:||02 May 2013|
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- Nanny Wermuth & D. R. Cox, 2008. "Distortion of effects caused by indirect confounding," Biometrika, Biometrika Trust, vol. 95(1), pages 17-33.
- Elena Stanghellini & Nanny Wermuth, 2005. "On the identification of path analysis models with one hidden variable," Biometrika, Biometrika Trust, vol. 92(2), pages 337-350, June.
- 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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