Covariate selection for identifying the causal effects of stochastic interventions using causal networks

DOI10.1016/j.jspi.2011.07.009zbMath1236.62155OpenAlexW2059660294MaRDI QIDQ643394

Na Shan, Jian-hua Guo

Publication date: 28 October 2011

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2011.07.009

zbMATH Keywords

identifiabilitycausal effectstochastic interventionback-door criterion

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90) Applications of statistics (62P99)

Related Items

An unbiased estimator of the causal effect on the variance based on the back-door criterion in Gaussian linear structural equation models ⋮ 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

Uses Software

• TETRAD

Cites Work

• Causation, prediction, and search
• Causal diagrams for empirical research
• Covariate Selection for Estimating the Causal Effect of Control Plans by Using Causal Diagrams
• Identifying independence in bayesian networks
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