This paper asserts that causality is an intriguing but controversial topic in philosophy, statistics, and educational and psychological research. By supporting the Causal Markov Condition and the faithfulness condition, Clark Glymour attempted to draw causal inferences from structural equation modeling. According to Glymour, in order to make causal interpretations of no experimental data, the researcher must do some type of manipulation, rather than conditioning, of variables. The Causal Markov Condition and its sister, the common cause principle, provide the assumptions to structure relationships among variables in the path model and to load different variables into common latent constructs in the factor model. In addition, the faithfulness condition rules out those models in which statistical independence relations follow was a result of special coincidences among the parameter values. The arguments against these assumptions by Nancy Cartwright as well as those for the assumptions by James Woodward are evaluated in this paper. (Contains 4 figures and 24 references.) (Author/SLD)