Graphical Criteria for Efficient Total Effect Estimation Via Adjustment in Causal Linear Models
因果线性模型中通过协变量调整高效估计总效应的图判据
Abstract Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided…