# False Cause **The fallacy:** Treating a real or perceived correlation between two things as evidence that one *caused* the other. The form: A and B occurred together (or in sequence) → A caused B. The Latin tags name two specific subforms: *post hoc ergo propter hoc* ("after this, therefore because of this" — sequence implies causation) and *cum hoc ergo propter hoc* ("with this, therefore because of this" — correlation implies causation). The fallacy is at the operational core of medical pseudoscience, conspiracy theory, and most political "the [policy] caused [outcome]" claims. It is also one of the most powerful, because the human brain is a relentless pattern-and-cause-detector — it produces causal stories from co-occurrence almost automatically, and the resulting feeling that the causation has been *seen* is hard to dislodge with statistical reasoning. ## The Mechanism **Innate causal-inference:** Humans evolved in environments where small samples and approximate causal reasoning were good enough — if the berries gave you a stomachache, you avoided that bush. The cognitive machinery overproduces causal inferences in environments where the events being correlated are produced by complex systems with many simultaneous influences (economies, public health, political outcomes). The system designed for "berry → stomachache" misfires badly on "tax cut → economic growth" or "vaccine → autism." **The correlation/causation gap:** Two events can be correlated for many reasons: A causes B; B causes A; both A and B are caused by some third factor C; the correlation is coincidence; the correlation is selection-effect from how the data was sampled. The fallacy treats the first explanation as if it were the only one, when in fact establishing causation requires ruling out all the others. **Why the fallacy persists:** Establishing actual causation is hard (controlled experiments, statistical adjustment, mechanism identification). Asserting causation is easy. In political and media environments where audiences won't audit the inference, asserting wins. ## KB Applications ### Anti-Vaccine Causal Inference The original autism-MMR claim is the textbook post-hoc fallacy. Symptoms of autism typically become apparent around the age children receive the MMR vaccine. Parents observe: vaccine, then symptoms; therefore vaccine caused symptoms. The temporal sequence is real; the causation isn't (decades of large-scale studies have ruled it out). [[Anti-Vax Movement]] is built on a foundation of post-hoc reasoning that statistical evidence has not been able to dislodge — because the post-hoc inference is cognitively prior to the statistical reasoning. ### "Biden Ruined the Economy" / "Trump Crashed the Economy" The standard partisan attribution: economic outcomes that occur during a president's term are causally attributed to the president, regardless of whether the president's policies actually drove them. Inflation that resulted from pandemic supply-chain disruption, oil prices set on global markets, and Federal Reserve policy decisions is reattributed to whoever happens to be in office. This is post-hoc reasoning at population scale — sequence (took office, inflation rose) gets read as causation (caused inflation), and the much more complex causal stack is ignored. ### "DEI Hire Crashed the Plane" A vivid contemporary example. When an aviation accident occurs, certain right-wing media voices reliably attribute it to diversity hiring practices — regardless of whether the actual investigation has identified hiring practices as a causal factor. The reasoning is: the airline has DEI policies; the airline had a crash; therefore DEI caused the crash. The actual NTSB findings, when they emerge, almost never support the inference. But by the time they emerge, the causal story has propagated and the audience has internalized it. ### Stop the Steal Pattern-Matching Some [[Stop the Steal]] arguments combine false-cause reasoning with Texas Sharpshooter cherry-picking: voting machines were manufactured by a particular company; the election outcome was unfavorable; therefore the company's machines caused the unfavorable outcome. The mechanism is unspecified; the causation is asserted from co-occurrence. The fact that the same machines counted Republican-favorable elections without complaint is filtered out. ## Defenses 1. **Demand the mechanism.** When a causal claim is made, ask: "*how* did A cause B? What is the actual mechanism?" If the speaker can only point to the correlation or sequence, the inference is unwarranted. 2. **List alternative explanations.** For any claimed A→B causation, list at least three alternatives: B→A, third factor causes both, coincidence, selection effect. The fallacy is operational when the speaker has not even considered these. 3. **Check for randomized or controlled evidence.** Real causation is established by controlled comparison, not by anecdote or correlation. If the evidence is "I noticed this pattern," it's not yet causal evidence. 4. **Apply [[Inversion]].** If the same causal logic were applied to a counterexample, would the speaker accept it? "Trump took office, the stock market rose, therefore Trump caused the stock market rise" — does the speaker who endorses this also endorse "Biden took office, unemployment fell, therefore Biden caused unemployment to fall"? Asymmetric application is diagnostic of motivated false-cause reasoning. ## See Also - Texas Sharpshooter — Frequently combined: cherry-pick the data that fits the false-cause story - Anecdotal — Single-case false-cause reasoning - [[Availability-Misweighing Tendency]] — Why vivid temporal sequences feel causal - [[Anti-Vax Movement]] — False-cause reasoning resistant to statistical correction