Correlation vs Causation: the budget decision every marketer gets wrong, including me, yeah also me, in the past.
This is long article includes 3 parts: the story, the trick (with explanation) & the treat (3 how-to & 3 reco).
1. THE STORY
You know the conversation, I think that way. We all know the untold.
So picture this: you’re sitting in a monthly performance review & someone pulls up the dashboard showing ROAS jumped 30% after increasing spend on a channel last month.
The energy in the room shifts immediately: everyone’s excited, nodding along, already talking about doubling down next quarter. Bravo!!!
And you’re sitting there feeling this weird tension because on paper it looks like a clear win, but something in your gut is saying “wait, hold on, this feels too clean. Maybe it is too good to be true, so unreal.”
- Maybe you remember there was a big promo running that week.
- Maybe it was right after payday when people have money to spend anyway.
- Maybe you noticed a competitor went dark around the same time.
You can’t quite articulate why the “winning channel” story feels off, so you stay quiet & the deck gets approved & 3 months later you’re looking at diminishing returns wondering what the hell happened 😂.
The problem wasn’t the data, it was that we were treating correlation like it was causation & making massive budget decisions based on that confusion, yo know.
Let me break down what these terms actually mean because I think most of us use them interchangeably when they’re completely different things
2. THE TRICK: Correlation vs Causation
Correlation is when two variables move together at the same time. Spend goes up, sales go up that’s just pure correlation.
It’s pure co-movement, no explanation required, just the observation that these two things seem to dance together in the data.
Correlation is a relationship where two variables move together. It is useful, it is fast, and it is often the first thing you see in a dashboard.
Correlation is a detection tool.
❎ It tells you what moves together
❌ It does not tell you what caused what
and it is symmetric: swapping X and Y does not change the correlation, which is exactly why it cannot, by itself, prove direction or causality.
Causation is way more specific & scientific.
It’s when changing X directly creates a change in Y. It is like if you turn off a channel & sales drop, then turn it back on & sales return with incrementality, & that’s causation.
You’ve got proof that X is actually influencing Y, not just coincidentally moving at the same time.
Modern ad systems make this trap worse because they are not neutral observers. They actively concentrate delivery where they predict outcomes.
👉That means your “exposed” group can be inherently different from your “not exposed” group before your campaign even starts.
This is why experiments exist in marketing: to test reaction hypotheses with the aim of determining cause and effect relationships (causation) with 3rd party verified, and to do it in real conditions when possible.
Again, remember my word: to test hypotheses finding cause-and-effect with 3rd party verified.
To test hypotheses and identify cause-and-effect relationships, with results verified by an independent 3rd party (Tommy)
And here’s the line that should be tattooed on every marketer’s forehead: correlation does not imply causation.
Again, correlation does not imply causation, not always.
I know everyone’s heard this before, but in practice, most marketing teams completely ignore it because dashboards make correlation look so damn convincing that it feels like causation, yo know 🤷♂️.
That’s why I wrote article tells: dashboard is for illustration, not telling 100% story, & dashboards don’t answer the whole picture, the unseen or of course, source of truth.
It is because online dashboard [rarely] show the linkage & clear illustration for causation.
When you actually need to prove causation like when you’re deciding where to allocate millions in budget. You need field experiments, not just patterns in your analytics.
Because patterns change constantly & dashboards are designed to make those lies look like strategic insights 😂.
3. THE TREAT for correlation & causation
So let me walk you through the three ways correlation creates causal illusions that trap smart marketers all the time
1️⃣ The first one is what researchers call confounding, but I just think of it as “the hidden third thing that’s actually driving everything.”
This happens when some other factor is moving both your input & your output at the same time, making it look like one caused the other when really they’re both just responding to this third variable you’re not tracking.
👉Seasonality is the worst version of this.
- Summer heat drives foot traffic which makes your paid social look efficient, but really people were already in buying mode because it’s summer.
- Holiday season lifts all your channels at once but you can’t tell which ones are actually adding incremental value versus just riding the seasonal wave.
- Back-to-school campaigns look amazing in August but parents were always going to buy school supplies whether they saw your ad or not, you’re just taking credit for demand that was baked in, yo know.
2️⃣ The second mechanism is reverse causality, which is when the story is literally running backwards in your data.
Sales increase first, then you increase budget after because you’re trying to capitalize on momentum, but when you look at the data later it shows spend going up & sales going up & your brain automatically reads that as “spend caused sales” when actually sales justified the spend.
👉I see this all the time with always-on campaigns.
Organic demand starts climbing because of word-of-mouth or something goes viral, you see early signals in the analytics & you’re smart so you increase paid spend to capture the wave.
The platforms are actually really good at this now. The algorithm detects rising purchase intent & automatically increases delivery to capitalize on it, then takes full credit for conversions that were probably going to happen anyway. You end up paying for demand you would’ve captured regardless, but correlation makes it look like you created that demand, yo know 🤷♂️🤷♂️🤷♂️
3️⃣ The third mechanism is selection bias, and this one’s getting worse as platform algorithms get better at optimization.
Basically ads get delivered to people who are already super close to converting, so ad exposure & conversion move together really strongly, but that doesn’t mean the exposure caused the conversion.
It just means the algorithm is really good at predicting who’s about to buy anyway & showing them ads right before they do.
👉Retargeting is the clearest example.
Your retargeting campaign shows incredible ROAS because it’s only hitting people who already visited your site, added products to cart & are probably coming back to complete the purchase whether they see another ad or not.
The correlation between seeing the retargeting ad & converting is crazy strong, but the actual incremental lift from showing that ad might be close to zero. It might be & can be linked to zero.
Because you’re essentially paying to show ads to people who were already on their way back to buy, but your dashboard tells you the retargeting “worked” because exposure & conversion happened.
Okay so here’s where I think most conversations about this topic go off the rails
People turn it into this philosophical debate about what’s “real” or whether we can ever truly know causation, and honestly that’s not helpful for people who just need to make budget decisions.
Fundamentally, that is just marketing after all, dealing with all mentality, thinking & logic mixing with emotions. (Tommy)
What we actually need is a practical framework for when correlation is good enough & when you absolutely need causal proof before making a call, yo know.
There are times when correlation is perfectly fine & you should just use it:
❎When you’re monitoring funnel health, correlation is great.
If you notice CTR dropping while CPM is spiking, you don’t need to run a causal experiment to know something’s wrong & worth investigating. The correlation breakdown itself is the signal.
❎When you’re forecasting future performance.
Correlation is actually better than causation because you’re trying to model co-movement patterns, not prove why things move. You just need to know that when X historically goes up, Y tends to follow, and that’s enough to build a forecast on.
❎When you’re doing anomaly detection, correlation works perfectly.
If spend increases but conversions don’t follow the usual pattern, that break in the correlation is telling you something changed & you need to dig deeper to figure out what, yo know.
But there are other times when you absolutely CANNOT make the decision without causal proof:
❌Budget allocation is the big one.
When you’re deciding which channels get more money & which get cut, you cannot make that call based on correlation alone because you’ll end up funding channels that look good in dashboards but aren’t actually driving incremental results.
I’ve seen this destroy marketing efficiency at multiple companies 😂, especially search ad!!!
❌Measuring incrementality is another must-have for causation.
When you’re trying to answer “what did this spend add that wouldn’t have happened anyway,” correlation literally cannot tell you that.
It can show you that spend & sales moved together, but it can’t tell you how much of those sales would have happened without the spend, yo know 🤷♂️. Take a look on display & social ad.
❌Scaling decisions require causation too.
When you’re trying to figure out if doubling spend will double results or just double waste, you need to know if the relationship is actually causal & lever-able or if you’re about to hit massive diminishing returns because you were never actually driving incremental demand in the first place.
This is enough for first part. Other part will come later.
To sum up, take this inforgraphic for recall!!
TOMMY 🙏
P/s: Opinions are my own. Please take consideration for your actions.
This is as for informational & educational purpose, No liability for actions taken. Nothing in this article constitutes legal, compliance, or regulatory advice.
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