Identification of causal effects in linear models: beyond instrumental variables

DOI10.1007/s11749-014-0421-3zbMath1326.62133OpenAlexW2045321507MaRDI QIDQ497856

Elena Stanghellini, Eduwin Pakpahan

Publication date: 25 September 2015

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11749-014-0421-3

zbMATH Keywords

identificationdirected acyclic graphlatent variablecausal effectstructural equation modelconfounderregression graph

Mathematics Subject Classification ID

Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (3)

Variance formulas for estimated mean response and predicted response with external intervention based on the back-door criterion in linear structural equation models ⋮ Exact variance formula for the estimated mean outcome with external intervention based on the front-door criterion in Gaussian linear structural equation models ⋮ Selection and integration of generalized instrumental variables for estimating total effects

Cites Work

• Identifiability of parameters in latent structure models with many observed variables
• Statistics and causal inference: A review. (With discussion)
• Sequences of regressions and their independences
• Identification of discrete concentration graph models with one hidden binary variable
• Measurement bias and effect restoration in causal inference
• Graphical identifiability criteria for causal effects in studies with an unobserved treatment/response variable
• Distortion of effects caused by indirect confounding
• Causal diagrams for empirical research
• The Theory of Parametric Identification
• On the identification of path analysis models with one hidden variable
• Identification in Parametric Models
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