Why correlation is not often used for outcome evaluations?

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Correlation is not often used for outcome evaluations for several reasons:
1. Causality: Correlation measures the strength and direction of the linear relationship between two variables but does not establish causality. Outcome evaluations typically aim to understand the causal impact of a program or intervention on the outcome of interest. Correlation alone cannot determine whether a program caused the observed changes in the outcome or if other factors are responsible.
2. Confounding variables: Correlation does not account for confounding variables, which are factors that may influence both the outcome and the variable being correlated. Confounding variables can lead to spurious correlations, where two variables appear to be correlated, but the relationship is actually due to the influence of a third variable. Outcome evaluations often strive to control for or account for confounding variables to establish a more accurate understanding of the program's impact.
3. Complex relationships: Correlation measures linear relationships, assuming that the relationship between variables can be adequately described by a straight line. However, many real-world relationships are more complex and may exhibit non-linear patterns or involve interactions between multiple variables. Correlation may fail to capture these complexities, limiting its usefulness for outcome evaluations.
4. Directionality: Correlation does not provide information about the direction of the relationship between variables. While it indicates the strength of the linear association, it does not indicate whether one variable is causing changes in the other or if the relationship is bidirectional.