Causal inference is a central topic in education research, although oftentimes it relies on observational studies, which makes causal identification methodologically challenging. This manuscript introduces causal graphs as a powerful language for elucidating causal theories and an effective tool for causal identification analysis. It discusses graphical criteria for causal identification, which provide principled approaches for removing bias and assessing causal identification given a causal theory. Through illustrative examples, this manuscript demonstrates the application of causal graphs and adjustment criterion for covariate selection in the context of education research, exemplifying their key advantages particularly in scenarios where randomized experiments are impractical. This manuscript aims to acquaint researchers with causal graphs as an effective tool for causal inference, thereby facilitating theory-based causal inquiries in applied education research.