The three classic criteria necessary to support a causal inference, according to the philosopher John Stuart Mill, are: (1) association (correlation), (2) temporal order, and (3) nonspuriousness. The criterion of association requires that there is a systematic relationship between the cause and effect variables.

In summary, before researchers can infer a causal relationship between two variables, three criteria are essential: empirical association, appropriate time order, and nonspuri- ousness. After these three conditions have been met, two other criteria are also important: causal mechanism and context.

Causal research, . is the investigation of (research into) cause-relationships. To determine causality, Variation in the variable presumed to influence the difference in another variable(s) must be detected, and then the variations from the other variable(s) must be calculated (s).

A causal relation between two events exists if the occurrence of the first causes the other. The first event is called the cause and the second event is called the effect. A correlation between two variables does not imply causation.

But, does a linear regression imply causation? The quick answer is, no. It is easy to find examples of non-related data that, after a regression calculation, do pass all sorts of statistical tests.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events.

To establish causality you need to show three things–that X came before Y, that the observed relationship between X and Y didn’t happen by chance alone, and that there is nothing else that accounts for the X -> Y relationship.

Examples of Cause and Effect Cause: We received seven inches of rain in four hours. Effect: The underpass was flooded. Cause: I never brush my teeth. Effect: I have 5 cavities.

Rather, all reported cases can be considered potentially drug-related, and causality is assessed by comparing the rates of reports in patients treated with test drug and in control groups. If an event is clearly more frequent with test drug than the control, it can be attributed to treatment with the test drug.

There are three criteria that must be met to establish a cause-effect relationship:

1. The cause must occur before the effect.
2. Whenever the cause occurs, the effect must also occur.
3. There must not be another factor that can explain the relationship between the cause and effect.

Things happen for a reason: there is a cause for every effect. In science, the cause explains why something happens. The effect is the description of what happened.