According to Suppes's probabilistic theory of causality, search for genuine cause is a philosophical issue. None of statistical methods can help determine the genuine cause. What statistical methods can do is to estimate effects of a presumed cause, no matter it is a genuine cause or not. Therefore, we should not focus on search of a cause of an effect, but on effects of a cause. I briefly address Rubin's experimental model to help clarify several major issues in causal inference. For instance, only through random assignments can estimated parameters possess causal meanings. Similarly, in observational studies, strong ignorability should be checked if parameters are to have causal meanings. I illustrate how to apply Rubin's model to research of school effectiveness through three examples. Since hierarchical linear models are commonly used in this area, I concisely introduce their theoretical grounds and parameter estimation procedures. If the necessary assumptions are sustained, parameters of hierarchical linear models can be used to depict school effectiveness.
| Keywords: | School Effectiveness; Causal Inference; Hierarchical Linear Models |
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