There are many ways of knowing, and different cultures and subcultures use different expectations and norms about proof and causality. Causality is critical: it tells us what is possible, what can be changed and what is difficult, if not impossible, to change. Causality tells us what are the “prime movers” of the phenomena that we observe.

Consider some different perspectives on causality:

• God (or some type of Gods) did it.
• Nature works with "an unseen hand".
• There are "rational laws" to be discovered (and people are capable of discovering these).
• Causal relations are an illusion; the universe is chaotic.

Here are some different ways and means of "proof":

• Controlled experiments in which purported causal factors are manipulated systematically.
• Citing recognized authorities, such as Biblical or Koran scripture-or Sigmund Freud.
• Marshalling one's reasonable arguments as in a court of law or journalism.
• Precedent as in a court of law.
• Intuition...feelings...one just "knows" (in love?).
• Reading traces in the environment (Sherlock Holmes).
• Devine revelation in dreams, visions, bones, tea leaves, etc.
• Statistically controlling various purported causal variables.

Establishing causality is by no means agreed-upon even among  scientists, let alone most members of a particular culture. If you subscribe, at least in part to the "rational laws" approach, you also probably accept controlled experiments, logic, "reasonable arguments,” and statistical control as suggestive of "proof." While science is one form of knowing and one generic way of gathering evidence that either disconfirms or is suggestive of causality, it is not the only way of doing so. The results of science may or may not be accurate, but without following "the rules" of science, most scientists do not believe one is "doing science." Considerable disagreement occurs between scientists and members of the general public because scientists don't make it clear how our methods of  "proof" differ from those commonly used among the general public (e.g., legal arguments).

According to science rules, definitive proof via empirical testing does not exist. Science uses the term "proof" (or, rather, "disproof") differently from the way attorneys or journalists do. Our measurements could be later shown to be contaminated by confounding factors. A correlation could have many causes, only some of which have been identified. Later work can show earlier causes to be spurious, that is, both cause and effect depend on some prior causal (often extraneous) variable. Statistics are NEVER EVER considered to "prove" anything although statistical results CAN disconfirm.
 

Cancerous Human Lung This dissection of human lung tissue shows light-colored cancerous tissue in the center of the photograph. While normal lung tissue is light pink in color, the tissue surrounding the cancer is black and airless, the result of a tarlike residue left by cigarette smoke. Lung cancer accounts for the largest percentage of cancer deaths in the United States, and cigarette smoking is directly responsible for the majority of these cases.  "Cancerous Human Lung," Microsoft(R) Encarta(R) 96 Encyclopedia. (c) 1993-1995 Microsoft Corporation. All rights reserved.

Most people--and most scientists--accept that smoking cigarettes causes lung cancer although the evidence (for humans) is strictly correlational rather than experimental. There are many topics where it is neither possible--nor desirable--to use the experimental method. To accept more correlational evidence it will help to examine the rules below. (SCL)

Many scientists believe that the ONLY way to establish causality is through randomized experiments. That is why so many educational research text books designate experiments–and only experiments--as “quantitative research.” However a moment’s reflection will convince you that this cannot be so. Most people now accept that cigarettes cause lung cancer (see Encarta selection above)–yet no society ever randomly assigned half its population to smoke cigarettes and the other half not. This is a conclusion based on correlational evidence, i.e., observing the systematic covariation of two (or more) variables. Cigarette smoking and lung cancer are both "naturalistic"  variables, i.e., we must accept the data as nature gave them to us (some authors call these "organismic" variables for "organic.") However, there is no doubt that the results from careful, well-controlled experiments are typically easier to interpret in causal terms than results from other methods. Non-experimental methods must use a variety of ways to establish causality and ultimately must use statistical control, rather than experimental control.

If one variable causes a second variable, they should correlate (have a real relationship). Causation implies correlation. However, two variables could be associated without having a causal relationship. This could occur because two variables are caused by a third variable. For example, the apparent correlation between ice cream consumption and the number of assaults occurs statistically because temperature causes both variables. Thus, correlation does NOT imply causation.

If one variable causes a second, the cause is the independent variable. Speaking more statistically, variation in the independent variables comes from sources outside our causal system.

If you can designate a distinct cause and effect, the relationship is called asymmetric.

Independent variables are often also called explanatory variables or predictors.

The effect is the dependent variable.

Dependent variables are also sometimes called criterion variables.

Two variables may be associated but we may be unable to designate cause and effect. These are symmetric relationships. Married people average better mental health than unmarried people. However, we don't know if marriage promotes mental health or mentally healthy people are more likely to marry.

(1) TIME ORDER. The independent variable came first in time, prior to the second variable.
EXAMPLE: Gender or race are fixed at birth.

(2) EASE OF CHANGE. The independent variable is harder to change. The dependent variable is easier to change.
EXAMPLE: One's gender is harder to change than an opinion or years of school.

(3) "MAJORITY RULE." The independent variable is the cause for most people.
EXAMPLE: Although some people become so fed up with their jobs that they return to school to train for a better job, most people complete their education prior to obtaining a regular year-round, full-time job.

(4) NECESSARY OR SUFFICIENT. If one variable is a necessary or sufficient condition for the other variable to occur, or a prerequisite for the second variable, then the first variable is the cause or independent variable.
EXAMPLES: A certain type of college degree is often required for certain jobs. At most universities, publications are a prerequisite for being awarded tenure.

(5)  GENERAL TO SPECIFIC. If two variables are on the same overall topic and one variable is quite general and the other is more specific, the general variable is usually the cause.
EXAMPLE: Overall ethnic intolerance influences attitudes toward Hispanics.

(6) THE "GIGGLE" OR "SANITY" FACTOR. If reversing the causal order of the two variables seems illogical and makes you laugh, reverse the causal order back.
EXAMPLES: We don't  believe choosing a specific college major or supporting a particuar political candidate determines one's gender.

September 4, 2000.
Susan Carol Losh