Me: “Hey Sarah, you know how we’re always looking at patient data to improve care? There’s a really important distinction we need to keep in mind when we see trends, and that’s the difference between correlation and causation.”

Sarah: “Oh, yeah? What’s the big deal?”

Me: “Well, let’s break it down.

Correlation

“Correlation is basically just about a relationship or an association between two things. When two things are correlated, it means that they tend to change together. When one goes up, the other might go up too, or maybe when one goes up, the other goes down. They move in some sort of predictable pattern.

“Think of it this way:

• Positive Correlation: If the number of ice cream sales goes up, and the number of drowning incidents also goes up, they’re positively correlated. They’re moving in the same direction.
• Negative Correlation: If the temperature outside goes up, and the sales of winter coats go down, they’re negatively correlated. They’re moving in opposite directions.
• No Correlation: If the price of bananas goes up, and the number of people wearing red shirts today goes up, there’s probably no correlation. They don’t seem to have any relationship.”

Causation

“Now, causation is a much stronger claim. It means that one thing directly causes another thing to happen. It’s a cause-and-effect relationship. If you do ‘A,’ then ‘B’ will happen because of ‘A.’

“Using our previous example:

• If I push a domino (A), and it knocks over another domino (B), then my pushing (A) caused the second domino to fall (B).
• If a patient takes a specific antibiotic (A) and their bacterial infection clears up (B), then the antibiotic (A) caused the infection to clear (B) (assuming it’s effective for that bacteria and other factors are controlled).”

Why is it misleading to argue that correlational data demonstrates a cause-effect relationship?

Me: “So, here’s the crucial part, Sarah, and this is where it gets really misleading if we’re not careful: Correlation absolutely does NOT imply causation. Just because two things are correlated doesn’t mean one causes the other.

“Let’s go back to our ice cream and drowning example. If ice cream sales and drowning incidents are both high in the summer, does eating ice cream cause people to drown? Of course not!

“The misleading part comes in because there’s often a third, hidden factor at play, or sometimes it’s just pure coincidence. In the ice cream/drowning case, the hidden factor is summer weather. Hot weather leads to more people eating ice cream and more people swimming, which unfortunately leads to more drowning incidents. Ice cream isn’t the cause of drowning; hot weather is the common underlying cause of both.

“Here’s why it’s dangerous to confuse them, especially in healthcare:

1. Misguided Interventions: If we see a correlation between, say, patients who use a certain health app and better blood sugar control, and we assume the app causes the improvement without further research, we might invest heavily in rolling out that app, only to find it doesn’t work for everyone. Maybe the people using the app were already more motivated to manage their diabetes, or they had better access to other resources. The app is correlated with better control, but not necessarily the sole cause.
2. Wasting Resources: We could spend money, time, and effort on solutions that aren’t actually addressing the root cause of a problem. If we think X causes Y, but X is merely correlated with Y, our intervention on X will be ineffective.
3. Drawing Incorrect Conclusions: It can lead us down the wrong path in research and clinical practice. We might miss the actual factors that are truly influencing patient outcomes.
4. Coincidence vs. Reality: Sometimes, correlations are just random chance. If we flip a coin enough times, we’ll see patterns, but those patterns don’t mean the coin ’causes’ a certain sequence. In large datasets, random correlations can appear.

“So, when we’re looking at data, especially with our new informatics tools, we might see fascinating correlations. That’s great! It tells us where to start looking. But to prove causation, we need more rigorous methods, like controlled studies, randomized controlled trials, or deep mechanistic understanding. We need to rule out those ‘third variables’ and ensure that the cause precedes the effect, and that there’s a plausible pathway for the effect to occur.

“Makes sense, Sarah?”

Sarah: “Wow, that actually clarifies a lot! So, correlation is a hint, but causation is the proof. Got it!”

Me: “Exactly! It’s a fundamental principle for interpreting data responsibly, especially when patient well-being is on the line.”