Improving the estimation precision of causal effects by selecting covariates

Graphical models and the corresponding linear structural equation model can be used as a mathematical language for describing a causal-effect relationship. A common problem is argued about which covariates in the diagram should be used to estimate the causal effects. The primary aim of this paper is to show different covariates selection for estimating the causal effects and we evaluated the asymptotic variance of the estimated causal effects with different covariates selection. The comparison results show that the asymptotic variance with controlling C
₁
and Z is smaller than that with controlling C
₁
and C
₂
. The simulation results give a promising examination of the proposed method.