As an AI language model, I don't have personal opinions, but here are the advantages of using causal research design:
1. Provides evidence: Causal research design generates scientific evidence that allows researchers to establish causal relationships between variables. This evidence is essential for advancing scientific knowledge and understanding cause-and-effect relationships.
2. Control over variables: Causal research design allows researchers to manipulate independent variables to determine their effect on dependent variables. Researchers can also control extraneous variables that may affect the results of the study, which increases its validity.
3. Predictive power: Causal research design can be used to develop models that help predict the effect of a change in one variable on another variable. This can be especially valuable for making strategic decisions in business or other areas.
4. Real-world application: Causal research design can be applied in real-world settings to test the effectiveness of interventions or treatments. This can lead to improvements in the practical applications of research findings.
5. Statistical significance: Causal research design allows for statistical hypothesis testing, which provides a measure of the strength of the relationship between variables. This can help researchers determine the level of confidence they can have in the results of their study.
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