$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes.

Three executives lost their jobs this month for making the same mistake. They presented data showing success after their initiatives were launched. Boards approved promotions. Then someone asked the one question nobody thought to ask: “Could something else explain this?” The sales spike coincided with a competitor going bankrupt. The satisfaction increase happened when a toxic manager quit. The correlation was real. The causation was fiction. This mistake derailed their careers.

But here's the good news: once you see how this works, you'll never unsee it. And you'll become the person in the room who spots these errors before they cost millions.

But first, you need to understand what makes this mistake so common—and why even smart people fall for it every single day.

What is Causal Thinking?

At its core, causal thinking is the practice of identifying genuine cause-and-effect relationships rather than settling for surface-level associations. It's asking not just “do these things happen together?” but “does one actually cause the other?”

This skill means you look beyond patterns and correlations to understand what's actually producing the outcomes you're seeing. When you think causally, you can spot the difference between coincidence, correlation, and true causation—a distinction that separates effective decision-makers from those who waste millions on solutions that were never going to work.

Loss of Causal Thinking Skills

Across every domain of professional life, this confusion costs fortunes and derails careers.

A SaaS company sees customer churn decrease after implementing new onboarding emails—and immediately scales it company-wide. What they missed: they launched the emails the same week their biggest competitor raised prices by 40%. The competitor's pricing reduced churn. But they'll never know, because they never asked the question. Six months later, when they face real churn issues, they keep doubling down on emails that never actually worked.

This happens outside of work too. You start taking a new vitamin, and two weeks later your energy improves. But you started taking it in early March—right when days got longer and you began going outside more. Was it the vitamin or the sunlight and exercise? Most people credit the vitamin without asking the question.

But here's the good news: once you understand how to think causally, these mistakes become obvious. And one of these five strategies can be used in your very next meeting—literally 30 seconds from now. Let me show you how.

How To Master Causal Thinking

Mastering causal thinking isn't about becoming a statistician or learning complex formulas. It's about developing five practical strategies that work together to reveal what's really driving results. These build on each other—starting with basic tests you can apply right now, and progressing to a complete system you can use for any decision.

Strategy 1: The Three Tests of True Causation

Think of these as your checklist for evaluating any causal claim.

The Three Tests:

Test #1 – Timing: Confirm the supposed cause actually happened before the effect. If traffic spiked Monday but you launched the campaign Tuesday, that campaign didn't cause it. The cause must always come before the effect.
Test #2 – Consistent Movement: When the supposed cause is present, does the effect reliably occur? When the cause is absent, does the effect disappear? Document instances where they occur together. Then examine situations where the cause is absent. If the effect happens just as often without the cause, you're looking at correlation, not causation.
Test #3 – Rule Out Alternatives: Think carefully about what else could explain what you're seeing. Actively try to disprove your idea rather than only looking for supporting evidence. If you can't eliminate other explanations, you don't have causation.

Strategy 2: Ask “Could Something Else Explain This?”

Here's a technique you can implement in the next 30 seconds that will immediately improve your causal thinking: whenever someone presents a causal claim, ask out loud: “Could something else explain this?”

This single question is remarkably powerful. It forces the speaker to consider hidden factors they ignored. It reveals whether they've actually done causal analysis or just noticed a correlation and declared victory. It shifts the conversation from assumption to examination.

Try it in your next meeting when someone says “We did X and Y improved.” Watch how often they haven't considered alternatives. Watch how often their confident causal claim becomes less certain when forced to address this simple question.

Most people present correlations as causations without even realizing it. Your question makes that leap visible. Suddenly they have to justify it with evidence or back down. It's not confrontational—it's curious. And curiosity is the foundation of good causal thinking.

Use it today. Use it every time someone attributes an outcome to a cause without ruling out alternatives.

That question leads us naturally to our next strategy—learning to identify what those “something elses” actually are.

Strategy 3: Hunt for Hidden Causes

A confounding variable is a third factor that influences both your suspected cause and your observed effect. It creates the illusion of a direct relationship where none exists.

Here's a simple example: ice cream sales and drowning deaths both increase during summer months. Does ice cream cause drowning? Obviously not. The confounding variable is warm weather, which causes both more ice cream purchases and more swimming.

Now here's the business version: A retail company sees both customer satisfaction and sales increase after renovating their stores. Does the renovation cause higher satisfaction? Maybe—but both also increased because they renovated during the holiday shopping season when people are generally happier and spending more anyway. Same logical structure. Same expensive mistake if they conclude renovations always boost satisfaction.

Map the Relationship: When you observe a correlation, write down your suspected cause and your observed effect. This visualization helps you spot gaps in your logic immediately.
Ask “What Else Changed?”: Think carefully about what other factors were present or changed during the same period. Make a written list so your brain doesn't skip over these hidden causes.
Search for Common Causes: Identify factors that could influence both variables at the same time. For instance, if both employee satisfaction and productivity increased, could several toxic managers have left the company?
Consider Time-Based and Environmental Factors: Examine seasons, business cycles, economic trends, reorganizations, leadership changes, and industry shifts that could affect multiple outcomes at once.
Test by Controlling Variables: If possible, create scenarios where you can control or account for potential hidden causes. Try analyzing subgroups where the hidden cause is absent, or run controlled A/B tests.

Once you can spot these hidden causes, you're ready to understand why your brain makes these mistakes in the first place. And this next one? It's probably happening in your head right now without you realizing it.

Strategy 4: Outsmart Your Brain's Shortcuts

Your brain is wired to see causal connections everywhere, even where none exist. This isn't a design flaw—it's a survival mechanism that kept your ancestors alive. But in the modern business world, this pattern-seeking instinct can mislead you.

Your brain wants simple causal stories. Reality is usually more complex. Once you know what to watch for, you can catch yourself before making these errors.

Catch Your Instant Explanations: When you observe a pattern, pause before declaring causation. Ask yourself: “Am I seeing causation because it's really there, or because my brain desperately needs an explanation?”
Fight Confirmation Bias: Actively search for information that challenges your causal idea, not just data that supports it. If you can't find contradicting evidence, you haven't looked hard enough.

Here's how this plays out: A manager believes remote work hurts productivity. She notices every time someone's late to a Zoom call. But she doesn't notice the three on-time people. She remembers the one missed deadline but forgets the five delivered early. Her brain is filtering reality to confirm what she already believes.

Question Your Compelling Stories: Be wary of explanations that sound too neat. If your causal explanation reads like a perfect success story, double-check it.
Don't See Patterns in Randomness: Three successful quarters in a row doesn't mean you've discovered a winning formula. It might just be a lucky streak. Always ask “Could this pattern occur by chance?”
Watch the ‘After Therefore Because' Trap: Every time you catch yourself thinking “we did X and then Y happened,” force yourself to consider alternative explanations. Ask yourself “What would I need to see to know this isn't causal?”

Now that you understand how your brain works, let's put this all together into a practical system you can use every time you need to make a high-stakes decision.

Strategy 5: The Five-Question Causation Check

Mastering causal thinking requires more than understanding principles—it demands a clear approach you can apply when the stakes are high and the pressure is on.

The Five-Question Causation Check:

Define the Relationship Clearly: Write out the specific causal claim you're evaluating with precision. “Social media advertising increases qualified leads by X%” is better than “marketing works.”
Verify the Basics: Does the cause come before the effect in time? Are they consistently related across different contexts? Are there possible alternative explanations?
Look for or Create Tests: Find situations where the supposed cause varies while other factors stay constant. The goal is isolation—can you isolate the variable you're testing from everything else that's changing?
Check if More Causes More: Does more of the cause lead to more of the effect? If doubling your ad spend doubles your conversions, that's stronger evidence than if the relationship is erratic.
Test Reversibility: If you remove the cause, does the effect disappear? If you reinstate the cause, does the effect return? This is why pilot programs and controlled rollbacks are so valuable.

Put It Into Practice

You now have the complete framework for causal thinking—five strategies that work together to reveal what's really causing what.

But here's what separates people who learn this from people who actually use it—one simple practice you can do this week that makes this framework automatic.

Practice Exercise: The Causation Audit

A practical and effective way to internalize these strategies is through practice with real-world scenarios from your actual work.

Here's how to conduct your own causal analysis:

Identify a Correlation from Your Work: Choose a recent pattern or causal claim that affects budgets or strategy.
State Your Causal Hypothesis: Write out your causal claim explicitly. Be specific about the supposed cause and the supposed effect.
Brainstorm Alternative Explanations: List at least five alternatives. Force yourself beyond the obvious first three.
Apply Your Three Tests: Evaluate whether your idea meets all three tests for causation. Did the cause come first? Do they consistently move together? Have you actually ruled out alternatives?
Design a Simple Test: If possible, design a test to isolate the variable you're testing. For example, have some account managers follow one approach while others don't, with otherwise similar conditions.
Share Your Analysis: Explain your reasoning to a colleague or manager. Teaching forces clarity and demonstrates analytical rigor.

With practice, you'll become skilled at spotting false causation and identifying true cause-and-effect relationships. This skill compounds over time, making you more valuable with every analysis you conduct.

So what does this actually get you? Let me paint the picture of what changes when you master this skill.

The Rewards

The rewards of mastering causal thinking are well worth the effort and will compound throughout your career.

You become immune to the most expensive mistakes in business—the ones where you solve the wrong problem perfectly. When everyone else is celebrating a correlation as success, you'll be asking the questions that reveal what's really driving outcomes. Imagine being in a meeting where leadership is about to allocate $2 million to scale an initiative, and you're the one who asks the question that reveals a competitor's bankruptcy actually caused the results. That's career-defining value.

Your strategic recommendations carry weight because they're based on actual causation rather than hopeful patterns. Leaders who can distinguish between correlation and causation make decisions that actually work. When your predictions prove accurate while others' fail, your credibility compounds—you become the person everyone turns to when stakes are high.

You develop the intellectual humility that marks exceptional leaders. Causal thinking teaches you to question your initial judgments, seek alternative explanations, and change your mind when evidence demands it. These qualities don't just make you a better thinker—they make you someone others trust with important decisions.

So take these strategies and practice them. Apply them in your daily work. Question causal claims, hunt for hidden causes, check your biases, and use the systematic process. This makes you a more effective decision-maker, a more credible advisor, and someone who spots opportunities and avoids disasters that others miss entirely.

And you'll become the person in the room everyone listens to when the stakes are high.

Your Thinking 101 Journey

In Episode 1, “Why Thinking Skills Matter Now More Than Ever,” we exposed the crisis: your thinking ability is collapsing, AI dependency is creating cognitive debt, and those who can't think independently will be left behind.

In Episode 2, “How To Improve Your Logical Reasoning Skills,” you learned to distinguish deductive certainty from inductive probability, calibrate your confidence to match your evidence, and stop treating patterns as proven facts.

Today, you learned how to distinguish true causation from mere correlation—saving yourself from expensive mistakes where you solve the wrong problem perfectly.

Up next—Episode 4: “Analogical Thinking—The Power of Comparison.” Your brain doesn't learn through pure logic—it learns by comparison. Every breakthrough idea came from someone who made an unexpected connection. You'll learn how to generate insights through analogy, recognize when comparisons break down, and spot when others use false analogies to manipulate you.

Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?

Because right now, while you've been watching this, someone just approved a million-dollar budget based on a correlation they mistook for causation. The only question is: will you be the one who catches it?

To learn more about mastering causal thinking, listen to this week's show: How To Master Causal Thinking.

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SOURCES CITED IN THIS EPISODE

Pathmetrics – Marketing Attribution Waste 5 Common Marketing Attribution Mistakes to Avoid. (2025). Pathmetrics. (Citing Proxima research on global marketing waste) https://www.pathmetrics.io/attribution/5-common-marketing-attribution-mistakes-to-avoid/
Harvard Business Review – Correlation vs Causation in Leadership Luca, M. (2021). Leaders: Stop Confusing Correlation with Causation. Harvard Business Review. https://hbr.org/2021/11/leaders-stop-confusing-correlation-with-causation
The CEO Project – Correlation vs Causation in Business Correlation vs Causation in Business. (2024). The CEO Project. https://theceoproject.com/correlation-vs-causation-in-business/
Nature Communications – Causality in Digital Medicine Glocker, B., Musolesi, M., Richens, J., & Uhler, C. (2021). Causality in digital medicine. Nature Communications, 12, 4993. https://www.nature.com/articles/s41467-021-25743-9
Stanford Social Innovation Review – The Case for Causal AI Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review. https://ssir.org/articles/entry/the_case_for_causal_ai

ADDITIONAL READING

On Causation and Decision-Making Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.

On Thinking Clearly Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.

On Statistical Reasoning Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

Note: All sources cited in this episode have been accessed and verified as of October 2025.

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