Variance formulas for estimated mean response and predicted response with external intervention based on the back-door criterion in linear structural equation models

DOI10.1007/s10182-020-00372-7zbMath1476.62159OpenAlexW3035054726MaRDI QIDQ2234735

Manabu Kuroki, Hisayoshi Nanmo

Publication date: 19 October 2021

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-020-00372-7

zbMATH Keywords

identificationcausal effectpath diagramdirected acyclic graph (DAG)structural causal model

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Applications of graph theory (05C90) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (2)

An unbiased estimator of the causal effect on the variance based on the back-door criterion in Gaussian 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

Cites Work

• Identification of causal effects in linear models: beyond instrumental variables
• Covariate selection for identifying the effects of a particular type of conditional plan using causal networks
• Covariate selection for identifying the causal effects of stochastic interventions using causal networks
• Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
• A generalized back-door criterion
• SELECTION OF POST-TREATMENT VARIABLES FOR ESTIMATING TOTAL EFFECT FROM EMPIRICAL RESEARCH
• Measurement bias and effect restoration in causal inference
• Covariate Selection for Estimating the Causal Effect of Control Plans by Using Causal Diagrams
• Graphical Identifiability Criteria for Total Effects in Studies with an Unobserved Response Variable
• Optimizing a control plan using a structural equation model with an application to statistical process analysis
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