Applying the adjustment set to estimate causal effect of data based on the causal graph model

Abstract

The purpose of this paper was to introduce the five limitations of the PROC CAUSALGRAPH procedure and estimate the causal effect of the data by using the adjustment set based on the causal graph model. The five limitations were as follows： ①the PROC CAUSALGRAPH procedure could not deal with the causal graph model of directed circles； ② the PROC CAUSALGRAPH procedure could not evaluate dynamic processing scheme； ③ causal effect identification was a population concept； ④ causal effect identification was a nonparametric concept； ⑤ the PROC CAUSALGRAPH procedure could not identify the causal effect in some causal graph models. The example was for a simulated data set， using the conventional multiple Logistic regression model analysis and the causal graph model analysis， respectively. By comparing the analysis results of the two， the following conclusions were drawn： ① causal graph theory was useful in identifying causal effects in confounding situations； ② by implementing hierarchical estimation of causal effects， a good statistical estimation of causal effects could be achieved based on the identification results of the PROC CAUSALGRAPH procedure.

Keywords

• causal graph model
• causal effect
• hierarchical estimation
• treatment effect