When one thing directly causes another, the relationship is called causation (also known as causality, or simply cause and effect). This is one of the most fundamental concepts in science, medicine, logic, and everyday thinking. It sounds simple, but the difference between one thing truly causing another and two things just happening to occur together is surprisingly tricky to pin down.
Causation vs. Correlation
Causation means that one event produces or brings about another event. Smoking causes an increased risk of lung cancer. Dropping a glass causes it to shatter. The first event (the cause) leads to the second event (the effect).
Correlation, on the other hand, means two things are statistically related but one doesn’t necessarily produce the other. Smoking is correlated with heavy drinking, for example, but smoking doesn’t cause alcoholism. The two just tend to show up together, possibly because of shared underlying factors like stress or personality traits. A correlation is a pattern. A causation is a mechanism.
This distinction matters because humans are wired to see causes everywhere. Ice cream sales and drowning deaths both rise in summer, but ice cream doesn’t cause drowning. Hot weather drives both. The thing driving both is called a confounding variable: a hidden third factor that creates the illusion of a direct link between two unrelated things. Confounders can strengthen, weaken, or completely fabricate an apparent relationship between two variables.
Related Terms You Might Be Looking For
Depending on the context, several other words describe the same basic idea of one thing causing another:
- Cause and effect: The everyday, plain-English version. One thing (the cause) produces a result (the effect).
- Etiology: In medicine, this refers to the study of what causes a disease or condition. When a doctor talks about the etiology of diabetes, they mean the factors that produce it, such as genetics, diet, or immune system dysfunction.
- Pathogenesis: A step beyond etiology. This describes the specific process by which a cause gets converted into a disease state.
- Determinism: In philosophy and physics, the idea that every event is the inevitable result of prior causes.
- Chain of causation: A sequence where A causes B, B causes C, and so on. Used in law, science, and accident investigation.
Proximate Causes vs. Ultimate Causes
Not all causes sit at the same level. Biologist Ernst Mayr drew a now-famous distinction between two types. Proximate causes are the immediate, mechanical triggers of an event. Ultimate causes are the deeper, historical reasons something exists in the first place.
Mayr used the migration of warblers as an example. The proximate cause of a warbler flying south in autumn is the dropping temperature and shorter days that trigger changes in its body. That’s the “how.” The ultimate cause is that, over millions of years, warblers that migrated south survived winters better than those that didn’t, shaping the species through natural selection. That’s the “why.”
This framework applies well beyond biology. If your car won’t start, the proximate cause might be a dead battery. The ultimate cause might be that you left the headlights on overnight, or that the battery is six years old and degraded.
Deterministic vs. Probabilistic Causation
Some causes always produce the same effect. If you heat water to 100°C at sea level, it boils. Every time, no exceptions. This is deterministic causation.
Most real-world causation, especially in health and human behavior, is probabilistic. Smoking causes lung cancer, but not every smoker develops it. The cause raises the probability of the effect without guaranteeing it. When scientists say “A causes B” in a probabilistic sense, they mean that A being present makes B significantly more likely than if A were absent. Deterministic causation rules out the possibility of “A happens but B doesn’t.” Probabilistic causation accepts that some people will smoke for decades and never get cancer, while still recognizing smoking as a genuine cause.
How Scientists Prove Causation
Establishing that one thing truly causes another requires more than noticing they happen together. The gold standard is a controlled experiment. You take a group, split it in two, expose one half to the suspected cause, keep everything else identical, and compare outcomes. If the exposed group shows a different result, you have strong evidence of causation.
When experiments aren’t possible (you can’t ethically assign people to smoke for 30 years), scientists rely on a set of criteria developed by epidemiologist Sir Austin Bradford Hill in the 1960s. These nine guidelines help researchers evaluate whether an observed association is likely causal:
- Strength: A stronger association is more likely to be causal.
- Consistency: The same result shows up across different studies and populations.
- Temporality: The cause must come before the effect. This is the only absolute requirement.
- Dose-response: More exposure leads to more effect (heavier smokers get more cancer).
- Plausibility: There’s a believable biological or physical mechanism.
- Coherence: The causal claim doesn’t conflict with what’s already known.
- Specificity: The cause leads to a particular effect, not everything at once.
- Experiment: Experimental evidence supports the link.
- Analogy: Similar causes are known to produce similar effects.
No single criterion proves causation on its own (except temporality, which is non-negotiable). The more criteria are met, the stronger the case.
The Most Common Mistake People Make
The logical error of assuming causation from sequence has its own Latin name: post hoc ergo propter hoc, meaning “after this, therefore because of this.” The Greeks and Romans identified this fallacy thousands of years ago, and it remains the most common source of false conclusions in everything from medical news to everyday arguments.
A child gets vaccinated and then develops a fever from an unrelated virus the next week. A company changes its logo and then sales drop due to a recession. In each case, the timing creates a powerful gut feeling of causation, but sequence alone proves nothing. The cause must come before the effect, yes, but coming before the effect doesn’t make something a cause.
Finding Causes in Practice
Outside of scientific research, people use structured methods to trace causes in professional settings. One of the simplest is the “5 Whys” technique: you state the problem, then ask “why?” five times in succession, each answer becoming the subject of the next question. If a machine stopped working, you ask why. The fuse blew. Why? It was overloaded. Why? The bearing seized. Why? It wasn’t lubricated. Why? The maintenance schedule was missed. You’ve moved from the surface symptom to a root cause you can actually fix.
A causal tree (sometimes called a fishbone diagram) works similarly but maps out multiple possible causes branching from a single outcome. These tools are standard in healthcare, engineering, and aviation safety, where understanding exactly what caused what can prevent the next failure.
Whether you call it causation, cause and effect, or etiology, the core concept is the same: one thing producing another, not just appearing alongside it. The word you’re most likely looking for is “causation” or “causality,” and the relationship between the two events is a “causal relationship.”