Abstract
This paper reviews basic ideas of Structural Causal Models (SCMs) proposed by Judea Pearl (1995, 2009a). SCMs are nonparametric structual equation models which express cause-effect relationship between variables, and justify matematical principles of both the potential outcome approach and the graphical model approach for statistical causal inference. In this paper, considering the difference/connection between SCMs and Rubin's Causal Models (RCMs) (Rubin, 1974, 1978, 2006), we state that (1) the expressive power of the potential outcome approach is higher than that of the graphical model approach, but (2) the graphical model approach. From these consderations, we conclude that we should discuss statistical causal inference based on both approaches.
