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Causal research, also called explanatory research, is the investigation of (research into) cause-and-effect relationships. To determine causality, it is important to observe variation in the variable assumed to cause the change in the other variable(s), and then measure the changes in the other variable(s). Other confounding influences must be controlled for so they don't distort the results, either by holding them constant in the experimental creation of data, or by using statistical methods. This type of research is very complex and the researcher can never be completely certain that there are no other factors influencing the causal relationship, especially when dealing with people’s attitudes and motivations. There are often much deeper psychological considerations that even the respondent may not be aware of.
Experiments are typically conducted in laboratories where many or all aspects of the experiment can be tightly controlled to avoid spurious results due to factors other than the hypothesized causative factor(s). Many studies in physics, for example, use this approach. Alternatively, field experiments can be performed, as with medical studies in which subjects may have a great many attributes that cannot be controlled for but in which at least the key hypothesized causative variables can be varied and some of the extraneous attributes can at least be measured. Field experiments also are sometimes used in economics, such as when two different groups of welfare recipients are given two alternative sets of incentives or opportunities to earn income and the resulting effect on their labor supply is investigated.
In areas such as economics, most empirical research is done on pre-existing data, often collected on a regular basis by a government. Multiple regression is a group of related statistical techniques that control for (attempt to avoid spurious influence from) various correlations other than the ones being studied. If the data show sufficient variation in the hypothesized explanatory variable of interest, its correlation if with the potentially influenced variable can be measured. This, however, does not imply a causal relationship.