Correlation vs. Causation Misuse
Correlation is the process of establishing a relationship between two or more factors. Correlation is an important concept that can be misused. One misuse is saying that factor A is caused by factor B just because correlation is found. Cause cannot be implied simply from correlation. Find two examples in scholarly articles within the last 10 years that use correlation analysis. One of the articles must use correlation to imply causation correctly and one article should not have justification to imply cause.
Establishing causation from correlation requires careful analysis to avoid misinterpretation. Below are two scholarly articles from the past decade that illustrate both appropriate and inappropriate uses of correlation in implying causation:
1. Appropriate Use of Correlation to Imply Causation:
In the article “Thinking Clearly About Correlations and Causation,” the authors discuss methods for making causal inferences from observational data. They introduce graphical causal models as a robust tool for distinguishing between mere correlations and genuine causal relationships. By utilizing these models, researchers can map out potential causal pathways and control for confounding variables, thereby providing a justified basis for inferring causation from observed correlations.
2. Misuse of Correlation to Imply Causation:
The study “Illusion of Causality in Visualized Data” highlights how data visualization can lead to erroneous causal interpretations. Through a series of experiments…
Establishing causation from correlation requires careful analysis to avoid misinterpretation. Below are two scholarly articles from the past decade that illustrate both appropriate and inappropriate uses of correlation in implying causation:
1. Appropriate Use of Correlation to Imply Causation:
In the article “Thinking Clearly About Correlations and Causation,” the authors discuss methods for making causal inferences from observational data. They introduce graphical causal models as a robust tool for distinguishing between mere correlations and genuine causal relationships. By utilizing these models, researchers can map out potential causal pathways and control for confounding variables, thereby providing a justified basis for inferring causation from observed correlations.
2. Misuse of Correlation to Imply Causation:
The study “Illusion of Causality in Visualized Data” highlights how data visualization can lead to erroneous causal interpretations. Through a series of experiments…
