The ‘They Happen Together!’ Brain | Why We Think Correlation is Causation (The Cum Hoc Fallacy)

The Cum Hoc Ergo Propter Hoc fallacy occurs when one assumes that because two things are correlated (occur together or in sequence), one must be the cause of the other. The ‘They Happen Together!’ Brain defaults to a Vibrant Gold simple causal link, ignoring the possibilities of coincidence, reverse causality, or a Fuchsia-pink hidden third variable. The very nice solution is the Deep Teal/Cyan Third Variable Test, which expands the search for causality to achieve Cheerful Mustard Yellow evidence-based conclusions.

Logic explains this through: The failure to satisfy the necessary conditions for establishing causality (temporal precedence, covariation, and non-spuriousness).

Coincidence is not a cause.

Madness Meter: 🌀🌀🌀 Causal Simplification (The reflexive need to link two concurrent events into a simple, single cause-and-effect chain.)

The Cum Hoc Ergo Propter Hoc fallacy is a specific type of non sequitur (“it does not follow”) where a conclusion about causality is drawn from evidence that only shows concurrence. Our brains are natural pattern-finders, but we are too quick to assume that the discovered pattern is a Vibrant Gold blueprint for cause and effect.

This creates the ‘They Happen Together!’ Brain | a mind that prioritizes narrative ease over statistical rigor. Establishing true causation is difficult—it requires controlled experiments, isolation of variables, and repeated testing. By contrast, simply noticing that two things happened at the same time is effortless, making the leap to causation a compelling Fuchsia-pink shortcut.

The core logical flaw is that a correlation between Event A and Event B leaves three critical possibilities unexamined:

1. Reverse Causation (Deep Teal/Cyan): B actually causes A, not the other way around.
2. Coincidence/Spurious Correlation (Vibrant Gold): The link is purely random (often seen in large datasets).
3. Third Variable/Confounding Factor (Fuchsia-pink): A third, unseen factor (C) is causing both A and B to occur.

S³ – Story • Stakes • Surprise

Story | Ice Cream and Drownings

The Classic Example: Research data consistently shows a strong correlation between the rise in ice cream sales and the rise in drownings.

The Fallacious Conclusion: Eating ice cream causes people to drown (or, less common, drownings cause people to crave ice cream).

The Causal Reality (Third Variable): The correlation is real, but neither factor causes the other. The Deep Teal/Cyan true cause is the Third Variable | Summer Temperature (C). Warmer weather (C) causes people to buy more ice cream (A) and also causes more people to swim, leading to more drownings (B). The Vibrant Gold two correlated factors are merely simultaneous effects of a single, unseen Fuchsia-pink root cause.

The Mechanism: Our ‘They Happen Together!’ Brain prefers the simple A $\to$ B link. We struggle to hold the more complex C $\to$ (A and B) model in our working memory, making the simpler, false causal narrative more compelling and easier to communicate.

Stakes | Bad Policy and Superstition

The unchecked power of the ‘They Happen Together!’ Brain has severe consequences:

Misinformation and Pseudoscience: The fallacy is the engine of anti-vaccination movements, supplement scams, and countless diet fads. When an illness follows a treatment, the brain instinctively links the two, even if the timing is coincidental.

Bad Business Decisions: A company might notice a Vibrant Gold correlation between employee social media use and high sales numbers. They might fallaciously conclude that social media causes high sales and increase their budget, when in fact, high sales (A) causes the employees to be happier and thus more likely to post on social media (B)—Fuchsia-pink Reverse Causation.

Political and Social Panics: Media reporting often confuses correlation for causation (e.g., claiming a rise in unemployment Deep Teal/Cyan causes a rise in crime, when both are caused by a separate economic recession). This leads to focusing resources on the wrong policy levers.

Surprise | The Third Variable Test

The very nice path is to systematically dismantle the simple A $to$ B link.

The Cure: Institute the Deep Teal/Cyan ‘Third Variable Test’ protocol:

1. Identify the Claim: Clearly state the assumed causal link (A causes B).
2. Test for Reverse Causation: Ask | “Could B cause A? (Does crime cause ice cream sales?)” If yes, the assumption is weak.
3. Search for Confounding Factor (C): Ask | “What is the one, single, external factor (C) that could plausibly cause both A and B to occur simultaneously?” (The shared C is often the most likely answer).
4. Isolate and Control: Only when the correlation is tested in a Fuchsia-pink controlled experiment that holds C constant and manipulates A (or vice versa) can causality begin to be established.

By committing to this systematic questioning of the assumed link, you gain the Cheerful Mustard Yellow clarity needed to move beyond mere coincidence and towards verifiable cause-and-effect.

A² – Apply • Amplify

Correlation measures a relationship; causation measures a consequence.

The Logic Bits

• Post Hoc Ergo Propter Hoc: A related fallacy (“after this, therefore on account of this”) which assumes that because A happened before B, A must have caused B.
• Spurious Correlation: A correlation between two variables that is purely coincidental. There are thousands of hilarious examples (e.g., US spending on science correlates with suicides by hanging).

Applying Anti-Causation Architecture

Adopt these Deep Teal/Cyan rules for rigorous analysis:

1. The “A, B, C” Mandate: When analyzing data, always create a Vibrant Gold three-column spreadsheet | A (Observed Event 1), B (Observed Event 2), and C (List of Plausible Third Variables). Never move to the conclusion before filling out C.
2. The ‘Control-Group Thinking’ Protocol: When someone claims a solution works (e.g., “This vitamin cured my cold”), immediately ask | “Compared to what? What happened to the Fuchsia-pink control group that did nothing?” If there is no control group, there is no proof of causation.
3. The ‘Correlation-Proof’ Language: Strictly avoid causal language (causes, proves, results in) when describing a correlation. Use only Cheerful Mustard Yellow correlational language (is linked to, is associated with, trends with).

The PSS Ecosystem | An Idea in Action

The PSS DAO can use awareness of the Cum Hoc fallacy to vet the effectiveness of its own governance incentives.

The ‘Causality Audit’ PSS Bounty

• Mechanism: Whenever a new PSS governance incentive is launched (e.g., an increase in rewards for participation) and is followed by a Vibrant Gold surge in activity, a specific Deep Teal/Cyan Causality Audit bounty is automatically issued.
• Justification: The audit is paid to search for Fuchsia-pink third variables that could explain the surge in activity (C) other than the incentive (A). Examples include a major market event (C), a marketing campaign outside the DAO (C), or a large social media mention (C). The DAO avoids falsely attributing the success solely to the incentive, which would lead to poor future budget decisions.
• Reward: A bonus PSS reward is given for successfully identifying a Cheerful Mustard Yellow dominant third variable that was initially overlooked, ensuring the DAO’s strategic decisions are based on genuine causal levers.

FAQ

Q | Does correlation ever imply causation A | Only when the correlation is found within a meticulously controlled experimental design (a Randomized Control Trial) that effectively rules out all other possibilities (third variables, reverse causation).

Q | What is a spurious correlation A | A correlation that is purely coincidental and has no logical, causal, or third-variable relationship. A famous example is the high correlation between the per capita consumption of mozzarella cheese and the number of civil engineering doctorates awarded.

Q | Why is this fallacy so common A | Our brains are survival machines. When two things happen, assuming A caused B allows for quick learning and prediction (e.g., “That berry caused that sickness”). This over-simplified, fast-thinking system often overrides the slow, critical-thinking system.

Citations & Caveats

• Source 1: Hume, D. (1739). A Treatise of Human Nature. (Philosophical work on the problem of induction and necessity of temporal succession for causality).
• Source 2: Pearl, J. (2009). Causality | Models, Reasoning and Inference. (A modern, technical analysis of formal causal inference methods).

Disclaimer: This article discusses the logical fallacy of Cum Hoc Ergo Propter Hoc. The PSS DAO token model described is theoretical and intended for conceptual discussion on improving critical thinking. Never mistake a pattern for a principle.