J. E. Aten, “Causal Not Confounded Gene Networks: Inferring Acyclic and Non-acyclic Gene Bayesian Networks in mRNA Expression Studies using Recursive V Structures, Genetic Variation, and Orthogonal Causal Anchor Structural Equation Models,” Ph.D. Dissertation, University of California, Oakland, 2008.

has been cited by the following article:

• TITLE: Confounding of Three Binary-Variable Counterfactual Model with DAG

  AUTHORS: Jingwei Liu, Shuang Hu

  KEYWORDS: Causal Effect; Independence Hypothesis; Counterfactual Model; Confounding Bias; Irrelevant; Ancillary Information; Directed Acyclic Graph

  JOURNAL NAME: Applied Mathematics, Vol.4 No.10, October 4, 2013

  ABSTRACT: Confounding of three binary-variable counterfactual model with directed acyclic graph (DAG) is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three causal counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses and ignorability, the sufficient conditions to determine whether the covariate variable is an irrelevant factor or whether there is no confounding in each counterfactual model are obtained.