Causation can only be determined from an appropriately designed experiment. In such experiments, similar groups receive different treatments, and the outcomes of each group are studied. We can only conclude that a treatment causes an effect if the groups have noticeably different outcomes.

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.

Inferring the cause of something has been described as:

1. “…
2. “Identification of the cause or causes of a phenomenon, by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes.”

18th century philosopher David Hume described three basic conditions that are necessary for cause and effect to be inferred: Cause and effect must occur close together in time. The cause must occur before the effect. The effect should never occur without the cause occurring first.

A conclusion drawn from a study designed in such a way that it is legitimate to infer ∗cause. Most people who use the term “causal conclusion” believe that an experiment, in which subjects are ∗randomly assigned to ∗control and ∗experimental groups, is the only ∗design from which researchers can properly infer cause.

By randomly assigning cases to different conditions, a causal conclusion can be made; in other words, we can say that differences in the response variable are caused by differences in the explanatory variable.

Causation is a complete chain of cause and effect. Correlation means that the given measurements tend to be associated with each other. Just because one measurement is associated with another, doesn’t mean it was caused by it. The more changes in a system, the harder it is to establish Causation.

The term causal effect is used quite often in the field of research and statistics. ‘ ‘Effect’ is usually brought on by a cause. Therefore, causal effect means that something has happened, or is happening, based on something that has occurred or is occurring.৪ ডিসেম্বর, ২০২০

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.

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. On the other hand, if there is a causal relationship between two variables, they must be correlated.

The situation that best describes the concept of causation is when one event happens because of another. An example of causation could be when a person plays a lot in a casino, and as a consequence lose all its money.১১ এপ্রিল, ২০১৮

the theoretical or asserted law that every event or phenomenon results from, or is the sequel of, some previous event or phenomenon, which being present, the other is certain to take place.