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How can you differentiate between causation and correlation in your findings?

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Understanding the relationship between variables is a cornerstone of Business Intelligence (BI). In your analysis, you'll often encounter two concepts: causation and correlation. Causation implies that one event is the result of another, while correlation indicates that two variables move together, but one does not necessarily cause the other. It's crucial to distinguish between them to make accurate predictions and informed decisions. Misinterpreting correlation as causation can lead to faulty strategies and business mishaps.

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Neha Wadhawan Agarwal

VP Credit Risk

1 Define Terms

To effectively differentiate between causation and correlation, you must first understand what each term means. Causation, or causal relationship, is when a change in one variable directly causes a change in another. For example, an increase in advertising budget causing a rise in sales. Correlation, on the other hand, is when two variables have a mutual relationship but are not the direct cause of each other's changes. For instance, ice cream sales and temperature might be correlated because they both increase during summer, but buying ice cream doesn't cause the temperature to rise.

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Neha Wadhawan Agarwal

VP Credit Risk
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Before we start analysing data, it’s always a good practice to understand each variable or a set of variable in terms of: - meaning or definition. For an industry, each variable has a business meaning which is essential for bringing data driven insights. - distribution of the variable: missing and outlier values, categories of the variable. - univariate analysis: for quantitative variables, looking at their max,min, average. This gives a good view on the variables and type, and potential relationships. For example, you have data on credit card acquisitions which has channel and income data. Once you have meaning and distribution information for these two variables, you can look to find if there is any relationship and it will be clear.

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2 Seek Patterns

When analyzing data, look for patterns that might indicate a causal relationship. Consistency in a cause-effect pattern over time can suggest causation. If an increase in one variable consistently leads to a change in another across different scenarios and time periods, there might be a direct link. However, if the relationship appears sporadic or only under certain conditions, it could merely be a correlation. Remember, just because two trends seem to follow each other doesn't mean one is causing the other.

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3 Test Hypotheses

To establish causation, you can conduct controlled experiments where you manipulate one variable to observe changes in another. This method allows you to isolate the effect of one factor while holding others constant. For example, if you want to test whether a new feature on your website increases user engagement, you could introduce it to a segment of users and compare their behavior with a control group that doesn't have the feature. If the group with the new feature shows a significant increase in engagement, you might infer a causal relationship.

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4 Consider Context

Context is key in distinguishing causation from correlation. External factors, historical data, and industry knowledge can provide insights into whether a relationship is causal or not. You should also consider the possibility of third variables that could be influencing both of the variables you're examining. For example, if there's an increase in both social media activity and sales, it's important to consider whether a seasonal event or marketing campaign might be affecting both, rather than one causing the other.

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5 Use Tools

Business Intelligence tools can help you analyze complex data sets to identify patterns and relationships. These tools often include features for statistical analysis, predictive modeling, and data visualization, which can aid in discerning between causation and correlation. By using these tools to perform regression analysis or to build decision trees, for example, you can better understand the dynamics between variables and make more informed conclusions about their relationships.

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Neha Wadhawan Agarwal

VP Credit Risk
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Various tool are available to check correlation and causation, and can be easily coded in python. For correlation, examples: - correlation coefficient: for strength of relationships. - Pearson correlation: for direction of relationship I.e., negative or positive. - regression - rank correlation - chi square test For causation: - the granger causality test - A/B testing

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6 Ask Experts

Lastly, consulting with experts in your industry or in the field of statistics can provide clarity on whether a relationship is likely to be causal or merely correlational. These professionals can offer insights based on experience and knowledge that may not be immediately apparent from the data alone. Their expertise can be invaluable in interpreting complex relationships and ensuring that your business decisions are based on accurate understandings of causality.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you differentiate between causation and correlation in your findings?

1

2

3

4

5

6

7

1 Define Terms

2 Seek Patterns

3 Test Hypotheses

4 Consider Context

5 Use Tools

6 Ask Experts

7 Here’s what else to consider

Business Intelligence

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How can you differentiate between causation and correlation in your findings?

1

2

3

4

5

6

7

1 Define Terms

2 Seek Patterns

3 Test Hypotheses

4 Consider Context

5 Use Tools

6 Ask Experts

7 Here’s what else to consider

Business Intelligence

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