Correlation vs Causation AI Prompt

I found a relationship between two things in my data. Help me figure out whether one causes the other or whether they just happen to move together.

Relationship found: {{relationship}} (e.g. 'Customers who receive more than 3 emails per month have 40% lower churn rates')

1. Explain the difference — with an example that makes it stick:
   - Correlation: two things move together
   - Causation: one thing makes the other happen
   - Classic example: ice cream sales and drowning rates both go up in summer. They are correlated. But ice cream does not cause drowning — hot weather causes both.
   - Now apply this logic to my specific relationship

2. The three possibilities for my relationship:
   - Option A: X causes Y directly (send more emails → customers stay longer)
   - Option B: Y causes X (customers who plan to stay engage with more emails — reverse causation)
   - Option C: Something else causes both (high-value customers both receive more targeted emails AND churn less — a third variable is driving both)
   For my specific relationship, which of these is most likely and why?

3. The self-selection problem (the most common trap in business data):
   - When we observe who receives treatment vs who does not, the groups are often not comparable
   - In my example: do all customers receive the same number of emails, or do certain types of customers get more? If engaged customers get more emails AND engaged customers churn less, we are confusing engagement for email effect.
   - Explain whether self-selection is a concern for my specific finding

4. How to test for causation:
   - The gold standard: a randomized experiment (A/B test) where some customers randomly get more emails and others do not
   - What a proper experiment would look like for my relationship
   - Without an experiment, what evidence would make me more confident the relationship is causal?

5. What to say to stakeholders:
   - How should I accurately describe this finding without overclaiming? Give me the exact phrasing to use.