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How to Avoid the Correlation-Causation Fallacy in Finance: A Quick Guide

 


# Correlation Does Not Imply Causation: A One Minute Perspective on Correlation vs. Causation


If you are interested in finance, you have probably encountered many graphs, charts, and statistics that show the relationship between two variables. For example, you might see a graph that shows how the stock market performance is correlated with the unemployment rate, or how the inflation rate is correlated with the consumer price index. But what do these correlations mean? And can we use them to make predictions or draw conclusions about the causes of financial phenomena?


## What is correlation?


Correlation is a measure of how closely two variables move together. It ranges from -1 to 1, where -1 means that the variables move in opposite directions, 0 means that there is no relationship, and 1 means that the variables move in the same direction. For example, if the correlation between the stock market and the unemployment rate is -0.8, it means that when the stock market goes up, the unemployment rate tends to go down, and vice versa.


## What is causation?


Causation is a stronger concept than correlation. It means that one variable directly affects or influences another variable. For example, if smoking causes lung cancer, it means that smoking increases the risk of developing lung cancer. Causation implies correlation, but not the other way around. That is, if A causes B, then A and B must be correlated, but if A and B are correlated, it does not mean that A causes B.


## Why does correlation not imply causation?


There are many reasons why correlation does not imply causation. Here are some of the most common ones:


- **Third variable problem**: Sometimes, two variables are correlated because they are both influenced by a third variable that is not accounted for. For example, ice cream sales and shark attacks are correlated, but not because ice cream causes shark attacks or vice versa. They are both influenced by the temperature, which affects people's behavior and the activity of sharks.

- **Directionality problem**: Sometimes, two variables are correlated, but it is not clear which one causes the other, or if they cause each other. For example, education and income are correlated, but does education cause income, or does income cause education, or do they both affect each other?

- **Spurious correlation**: Sometimes, two variables are correlated by chance or due to some hidden factor that is not relevant to the analysis. For example, the number of people who drowned in pools and the number of films that Nicolas Cage appeared in are correlated, but this is most likely a coincidence or due to some other factor that has nothing to do with either variable.


## How can we determine causation?


Determining causation is not easy, and often requires more than just looking at correlations. Some of the methods that can help us establish causation are:


- **Experimental design**: The best way to determine causation is to conduct a controlled experiment, where we manipulate one variable and observe the effect on another variable, while holding everything else constant. For example, to test if smoking causes lung cancer, we can randomly assign some people to smoke and some people to not smoke, and compare their lung cancer rates after some time.

- **Statistical tests**: There are some statistical tests that can help us infer causation from correlation, such as the Granger causality test or the instrumental variable method. These tests rely on some assumptions and conditions that must be met, and they can only provide evidence, not proof, of causation.

- **Causal criteria**: There are some criteria that can help us evaluate the plausibility of causation, such as the Bradford Hill criteria or the counterfactual approach. These criteria include factors such as the strength, consistency, specificity, temporality, coherence, and mechanism of the causal relationship.


## Conclusion


Correlation and causation are two related but different concepts that are often confused or misinterpreted in finance and other fields. Correlation is a measure of how closely two variables move together, while causation is a stronger concept that means that one variable directly affects or influences another variable. Correlation does not imply causation, because there may be other factors that explain the relationship between two variables, such as a third variable, a reverse direction, or a spurious correlation. To determine causation, we need to use more rigorous methods, such as experimental design, statistical tests, or causal criteria. By understanding the difference between correlation and causation, we can avoid making false or misleading claims or predictions based on correlations, and instead use them as a starting point for further investigation and analysis.


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