Definition

False cause is an informal logical fallacy in which someone concludes that one thing caused another without sufficient evidence. In Latin it is often labeled non causa pro causa (“non-cause for cause”). Related textbook names include causal fallacy, faulty causation, and questionable cause.

So what is the false cause fallacy? It is not “A and B are unrelated, full stop.” Real causes exist. The error is treating sequence, coincidence, or correlation as if they already proved a causal link.

Highly illogical argument

The most cited form is post hoc ergo propter hoc—Latin for “after this, therefore because of this.” What does post hoc ergo propter hoc mean in practice? If B follows A, the arguer treats A as the cause of B on order alone:

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Event A happened.
Event B happened after A.
Therefore, A caused B.

That pattern is tempting because genuine causes do precede their effects. Precedence is necessary for many causal stories, but it is never enough by itself.

Post Hoc vs Cum Hoc

What is the difference between post hoc and cum hoc? Timing.

Form Latin gloss Timing cue Typical move
Post hoc after this, therefore because of this A clearly before B Sequence → cause
Cum hoc with this, therefore because of this Same time / co-movement Correlation → cause

Post hoc example:

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I wore my lucky socks, then we won.
The socks caused the win.

Cum hoc example:

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Ice cream sales and drowning deaths both rise in summer.
Ice cream causes drowning.

In the ice-cream case, a third factor—hot weather—helps explain both. Confusing co-occurrence for causation is cum hoc; blaming yesterday’s ritual for today’s outcome is classic post hoc.

Correlation vs Causation

What is the difference between correlation and causation?

  • Correlation — when one variable changes, another tends to change with it. It is a statistical association.
  • Causation — a change in one variable produces a change in the other, usually via a mechanism you can describe and test.

Correlation does not imply causation. Two series can track each other because of coincidence, reverse causation, or a shared cause. Causal claims need more than a matching chart: timing that fits a mechanism, controls for confounders, and ideally evidence that intervening on A changes B.

A related trap from your hasty generalization family is leaping from a vivid anecdote to a causal rule. False cause is specifically about the causal leap; hasty generalization is about the sample leap. They often travel together.

Common Variants

False cause is an umbrella. On this site it sits under Questionable cause with several subtypes:

Coincidence and superstition

A precedes B once—or a few memorable times—and the mind installs a causal story. Sports rituals, lucky charms, and “I washed the car, so it rained” belong here. The pattern feels predictive because memory keeps the hits and forgets the misses (survivorship / vividness effects).

Ignoring a common cause

A and B rise together because C drives both. Shoe size correlates with reading skill in children; growth (age) is the shared driver, not footwear causing literacy.

Reverse causation (wrong direction)

The speaker flips cause and effect. Streetlights come on as the sun sets; the lights do not cause sunset. Higher gun ownership may track crime rates because fear of crime drives purchases, not only because guns drive crime—direction has to be argued, not assumed.

Single-cause oversimplification

Complex outcomes are pinned on one villain. A company fails “because of one bad CEO,” when competition, costs, regulation, and market shifts may all matter. Reduction to one cause is still a causal error when the evidence only supports a partial story.

Regression and noise

Extreme results often move back toward the average on their own. Celebrating a policy because a spike “fixed itself” after the policy can be a regression fallacy: the intervention rode natural fluctuation.

Examples

What are examples of the false cause fallacy?

Example 1 — Everyday post hoc

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A tenant moves in.
The building furnace fails the next week.
Management: “The new tenant caused the furnace problem.”

Sequence is real; causation is not established. Furnaces fail on schedules unrelated to who signed the lease.

Example 2 — Media and culture

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A study finds that people who listen to heavy metal
report more aggressive feelings on a survey.
Conclusion: heavy metal causes violence.

The study may show association. Personality, environment, and selection into the music can explain the link. Jumping to “music → violence” is false cause unless the causal path is tested.

Example 3 — Policy and public health

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Person receives a vaccine.
Days later they report a health complaint.
Conclusion: the vaccine caused the complaint.

Adverse events can be causal, coincidental, or due to another factor. Reporting that something happened after vaccination is not, by itself, evidence that vaccination caused it. Good causal claims need rates, controls, and mechanisms—not only anecdotes in sequence.

Example 4 — Cum hoc with a confounder

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Cities with more firefighters have more fire damage.
Therefore firefighters cause worse fires.

Larger, denser cities tend to have both more firefighters and more opportunity for costly fires. The common urban factor confounds the naive causal story.

How to Spot and Respond

How can I spot or respond to a false cause argument?

  1. Name the evidence type — Is the speaker offering only “A before B” or “A with B,” or also a mechanism and comparative data?
  2. List alternatives — Coincidence, reverse causation, third cause, multiple causes, regression to the mean.
  3. Ask for discriminators — What would we see if A truly caused B? What would we see if C caused both?
  4. Separate claim from proof — Pointing out a false-cause argument does not automatically prove the conclusion false (that would risk a fallacy fallacy). It means the causal case is still open.

A short reply that keeps the discussion technical:

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“B followed A, but that only shows order.
What evidence rules out coincidence or a shared cause?”
  • Slippery slope — predicts a causal chain of worsening steps. Often builds on weak causal links between each step; false cause is the local bad link.
  • Hasty generalization — overgeneralizes from thin samples; often supplies the anecdotes that feed post hoc stories.
  • Conspiracy theory framing — may treat coincidences as proof of hidden causal agency.
  • Magical thinking — asserts causal powers without a testable mechanism (rituals, charms, symbolic acts).