Identification of causal effects in linear models: beyond instrumental variables

Elena Stanghellini and Eduwin Pakpahan

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2015, vol. 24, issue 3, 489-509

Abstract: 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. By exploiting the notion of Markov equivalence, the derivations can also be applied to regression graphs, thereby enlarging the class of models to which the results are of use. Copyright Sociedad de Estadística e Investigación Operativa 2015

Keywords: Causal effect; Confounder; Directed acyclic graph; Identification; Latent variable; Regression graph; Structural equation model; Primary 62H99; Secondary 62H20 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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Working Paper: Identification of casual effects in linear models: beyond Instrumental Variables (2013)
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DOI: 10.1007/s11749-014-0421-3

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