Confounding of Three Binary-Variable Counterfactual Model with DAG  

作　　者：Jingwei Liu Shuang Hu 

机构地区：[1]School of Mathematics and System Sciences, Beihang University, Beijing, China

出　　处：《Applied Mathematics》2013年第10期1397-1404,共8页应用数学（英文）

摘　　要：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.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.

关 键 词：CAUSAL Effect INDEPENDENCE Hypothesis COUNTERFACTUAL Model CONFOUNDING Bias Irrelevant Ancillary Information Directed ACYCLIC Graph 

分 类 号：R73[医药卫生—肿瘤]