Machine Learning Missteps: Why Understanding Causation vs Correlation Can Save Your Startup

Picture a founder in her late 20s. She's recently raised her first seed round for a wellness tech startup. Her team is small but passionate. Mornings are electric—productivity is sky-high, ideas fly across the room, and momentum feels unstoppable. She notices a trend: it always seems to start after their first round of espresso.

Curious and wanting to keep morale high, she upgrades the office. In comes a $3,000 espresso machine. She adds oat milk, barista training, the whole works. But something strange happens—afternoon productivity remains flat. In fact, team energy still drops off by 2pm. No matter how many espresso shots are poured.

She didn’t realize it then, but she had just fallen into a trap that many AI systems—and founders—make every day: confusing correlation with causation.

Machine learning (ML) is the engine behind modern AI. It identifies patterns in large datasets and makes predictions. Whether it’s recommending your next binge-worthy series or flagging fraudulent transactions, ML models use correlation-based logic—they detect what tends to happen together.

🔹 Correlation-based ML: Finds relationships between variables (e.g., “People who buy running shoes also buy protein powder”).

🔹 Causation-based ML: Understands what causes outcomes (e.g., “This medical treatment directly reduces heart attack risk”).

This distinction might seem subtle, but for startups building AI-driven products or making decisions based on data, it’s everything.

Without knowing the why behind a result, founders risk building the wrong solutions—or worse, reinforcing existing biases.

Real-World Failure: Amazon’s Hiring AI

In 2018, Amazon’s experimental AI tool for hiring began ranking male candidates higher than women. Why? It had trained on a decade of historical data, which reflected gender imbalance in tech hiring. The AI model spotted a pattern (correlation: "Most past hires were men") and assumed it was causation ("Men perform better in tech").

The result? The model downgraded resumes that included the word “women’s,” like “women’s coding club.” Amazon scrapped the system after realizing the bias couldn’t be removed.

👉 Lesson: Even with the best tech, misunderstanding data relationships leads to flawed decisions.

📊 Stat to Know: According to the World Economic Forum, 85% of AI projects fail to deliver expected results, often due to poor data assumptions or unclear objectives.

Let’s pivot to a win.

Real Success: Causal AI in Healthcare

At Mount Sinai Hospital, researchers used causal AI to better understand heart attack risks. Traditional models showed high cholesterol as a red flag. But with causal AI, they uncovered something deeper—a specific gene mutation caused both high cholesterol and increased heart attack risk.

Thanks to this insight, doctors could offer more personalized, preventative care beyond just recommending lifestyle changes.

🔍 Causal AI gave clarity where correlation couldn’t. It transformed care from reactive to proactive.

Whether you run a wellness brand, a SaaS platform, or a marketplace app, you’re probably making decisions using data. The question is—are those decisions rooted in causation or just correlation?

Here’s how to build smarter, cause-based AI strategy into your business:

Step 1: Ask “Why,” Not Just “What”

When your analytics or AI tool shows a pattern, dig deeper.

• What’s the hidden driver?

• Is there a third variable at play?

• Would this still happen if conditions changed?

🔁 Example: Your AI shows customers who read your blog also convert more. Great, right? But are they converting because of the blog… or because readers tend to be more engaged buyers to begin with?

Don’t stop at the data—challenge it.

Step 2: Run A/B Tests to Confirm Causation

A/B testing lets you isolate variables.

Let’s say your AI suggests that adding urgency ("Only 3 left!") increases sales.

You create two groups:

• Group A sees the message.

• Group B doesn’t.

If conversions rise in Group A only, you have evidence of causation.

📌 Tip: Use tools like Google Optimize, Optimizely, or even simple email A/B tests to validate assumptions.

Step 3: Use Causal AI for High-Stakes Areas

Not all AI tools are created equal. For mission-critical decisions—pricing, retention, treatment recommendations—use platforms built for causal inference.

Some tools include:

• DoWhy (Microsoft)

• CausalNex (QuantumBlack/McKinsey)

• DoubleML

These models attempt to mimic randomized control trials using data, helping you find what really drives change.

🔹 Example: Causal AI helped a fintech startup identify that notifications—not incentives—led to better loan repayment. They shifted strategy and saw default rates drop by 12%.

Step 4: Be Mindful of Bias in Your Data

Bias is sneaky. If your dataset reflects past inequalities or incomplete behavior, your AI will learn from it.

📌 Ask:

• Who is missing from this data?

• Does this dataset reflect what I want to continue, or what I want to change?

One quote sums it up best:

“AI is not unbiased. It’s a reflection of the data it’s fed and the humans who design it.” — Dr. Timnit Gebru, former co-lead of Google’s Ethical AI team

Founders should be especially mindful when building products for underrepresented groups. What’s missing from the data may be just as important as what’s present.

Step 5: Educate Your Team (and Investors)

Data literacy is leadership. Founders don’t need to code, but they should understand basic AI logic.

Start with:

• The difference between correlation and causation

• When A/B testing is needed

• What causal models do differently

Bring this language into board meetings and product standups. It’s not just for data scientists—it’s for everyone shaping the product.

AI is a powerful ally—but only when we understand how it works and where it can mislead us.

As a founder or creator, your job isn’t to know every algorithm. It’s to lead with clarity, curiosity, and courage. To ask better questions. To build products that reflect reality, not assumptions.

Because here’s the truth:

🚫 Coffee didn’t cause productivity.

🚫 Male resumes didn’t cause success.

🚫 Popular items don’t always cause more sales.

But asking why—and building models that can answer that—might just change your business trajectory.

Let’s build AI that isn’t just smart—but thoughtful.

📌 Use AI responsibly. The more power it has, the more intentional we need to be with its design and deployment.

💬 Continue the conversation: What assumptions have you challenged lately? Let’s talk it out on [Instagram/Twitter/LinkedIn].