Natural Experiment

A natural experiment is an observational study that takes advantage of a naturally occurring event or situation that can be exploited by a researcher to answer a particular question. Natural experiments are often used to study situations in which a true experiment is not possible, for instance, if the exposure of interest cannot be practically or ethically assigned to research subjects. Situations that may create appropriate circumstances for a natural experiment include policy changes, weather events, or natural disasters. This entry describes natural experiments, examines the limitations to such experiments that exist as a result of confounding, and discusses the use of instrumental variables to control confounding.

The key features of experimental study designs are manipulation and control. Manipulation, in this context, means that the experimenter can control which research subjects receive which exposures: For instance, those randomized to the treatment arm of an experiment typically receive treatment from the drug or therapy that is the focus of the experiment, while those in the control group receive no treatment or a different treatment. Control is most readily accomplished through random assignment, which means that the procedures by which participants are assigned to a treatment and control condition ensure that each has equal probability of assignment to either group. Random assignment ensures that individual characteristics or experiences that might confound the treatment results are, on average, evenly distributed between the two groups. In summary, then, an experiment is a study in which at least one variable is manipulated and units are randomly assigned to the different levels or categories of the manipulated variables.

Although the gold standard for epidemiologic research is often considered to be the randomized control trial, this design can answer only certain types of epidemiologic questions, and it is not useful in the investigation of questions for which random assignment is either impracticable or unethical. The bulk of [Page 720]epidemiologic research relies on observational data, which raises issues in drawing causal inferences from the results. A core assumption for drawing causal inference is that the average outcome of the group exposed to one treatment regimen represents the average outcome the other group would have had if they had been exposed to the same treatment regimen. If treatment is not randomly assigned, as in case of observational studies, the assumption that the two groups are exchangeable (on both known and unknown confounders) cannot simply be assumed to be true.

For instance, suppose an investigator is interested in the effect of poor housing on health. Because it is neither practical nor ethical to randomize people to variable housing conditions, this subject is difficult to study using an experimental approach. However, if a housing policy change such as a lottery for subsidized mortgages was enacted that enabled some people to move to more desirable housing while leaving other similar people in their previous substandard housing, it might be possible to use that policy change to study the effect of housing change on health outcomes. One well-known natural experiment occurred in Helena, Montana, where smoking was banned from all public places for a 6-month period. The investigators reported a 60% drop in heart attacks for study area during the time the ban was in effect.

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