natural experiment
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What is a natural experiment?
A natural experiment is an observational study that exploits an event or situation allowing seemingly random assignment of subjects into different groups. It's useful when controlled experiments are impractical or unethical, such as studying policy changes or natural disasters. Common fields using natural experiments are epidemiology, political science, psychology, and social science.
Natural experiments are quasi-experiments because they do not randomly assign participants into groups. This means that factors such as attrition, history, testing, regression, instrumentation, and maturation can affect the results. While they can't definitively determine causation, they offer valuable data for research questions that might not be obtainable otherwise.
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What is an observational study?
An observational study is a type of research in which behaviors are watched and recorded. These observations can give information that may not be obtainable through other methods.
In observational studies, researchers do not manipulate variables but rather observe and analyze naturally occurring situations or events. Unlike controlled experimental designs, observational studies lack manipulation and random assignment, making it more difficult to draw causal inferences.
Epidemiology commonly uses observational data because random assignment is either impractical or unethical. Types of observational studies include cross-sectional, case-control, and cohort studies.
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How is a natural experiment different from a regular experiment?
A natural experiment is an observational study that uses naturally occurring situations, like policy changes or weather events, to create conditions similar to a randomized experiment. Researchers David Card, Joshua Angrist, and Guido Imbens showed that these "natural experiments" can clarify causal relationships in social sciences, where typical controlled experiments are impossible.
Unlike regular experiments, natural experiments do not involve researchers assigning subjects to different groups. Instead, they rely on naturally occurring events that create a seemingly random assignment. Because of the lack of participant randomization, researchers must consider factors, such as history and maturation, that might affect the results.
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How do researchers collect data in an observational study?
In a natural experiment, an observational study uses naturally occurring situations to create conditions similar to a randomized experiment. Because researchers do not assign subjects to groups, they use primary and secondary data sources to calculate rates and conduct studies. Primary data is collected for a specific purpose by the investigator, while secondary data was collected for another purpose by other organizations. Modern technology, like computers and electronic sensors, has automated the collection and detailed analysis of observational data.
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Can you give an example of a situation that might be studied as a natural experiment?
A natural experiment is an observational study that exploits a situation where study subjects are assigned to different groups randomly or seemingly randomly. These experiments help answer questions when controlled experimentation is impossible due to practical or ethical reasons.
For example, researchers could study the impact of a new housing policy, such as a lottery for subsidized mortgages, on health outcomes. Some people move to better housing because of the policy, while others stay in substandard housing. This situation allows researchers to study the effects of improved housing on health without assigning people to different housing conditions, which would be impractical and unethical. Another natural experiment example is the effect of a smoking ban in public places on heart attack rates.
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What are some of the benefits and challenges of using observational studies and natural experiments in science?
Both observational studies and natural experiments offer unique avenues for scientific inquiry, each with its own set of benefits and challenges.
Benefits: Natural experiments are valuable when controlled experiments are not feasible due to ethical or practical constraints. They allow researchers to study real-world situations, such as policy changes or natural disasters, providing insights into phenomena as they naturally occur. Observational studies, including behavioral assessments, allow for the collection of data that cannot be obtained through other methods. Case-control studies are particularly useful for studying rare diseases, as they require smaller sample sizes, and can be conducted in less time.
Challenges: Natural experiments, as quasi-experiments, do not randomly assign participants to exposure groups, which introduces threats to causal inference. Observational studies are susceptible to bias, which can arise from poor selection of cases and controls, or from unequal recall of exposures between groups. Additionally, the value of behavioral assessment depends on the behaviors selected for observation.
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natural experiment, observational study in which an event or a situation that allows for the random or seemingly random assignment of study subjects to different groups is exploited to answer a particular question. Natural experiments are often used to study situations in which controlled experimentation is not possible, such as when an 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, and natural disasters. Natural experiments are used most commonly in the fields of epidemiology, political science, psychology, and social science.
Comparison with controlled study design
Key features of experimental study design include manipulation and control. Manipulation, in this context, means that the experimenter can control which research subjects receive which exposures. For instance, subjects randomized to the treatment arm of an experiment typically receive treatment with 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 this way, at least one variable can be manipulated, and units are randomly assigned to the different levels or categories of the manipulated variables.
In epidemiology, the gold standard in research design generally is considered to be the randomized control trial (RCT). RCTs, however, can answer only certain types of epidemiologic questions, and they are not useful in the investigation of questions for which random assignment is either impracticable or unethical. The bulk of 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 the case of observational studies, the assumption that the two groups are exchangeable (on both known and unknown confounders) cannot be assumed to be true.
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As an example, suppose that 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. In another example, a well-known natural experiment in Helena, Montana, smoking was banned from all public places for a six-month period. Investigators later reported a 60-percent drop in heart attacks for the study area during the time the ban was in effect.
Natural experiments as quasi experiments
Because natural experiments do not randomize participants into exposure groups, the assumptions and analytical techniques customarily applied to experimental designs are not valid for them. Rather, natural experiments are quasi experiments and must be thought about and analyzed as such. The lack of random assignment means multiple threats to causal inference, including attrition, history, testing, regression, instrumentation, and maturation, may influence observed study outcomes. For this reason, natural experiments will never unequivocally determine causation in a given situation. Nevertheless, they are a useful method for researchers, and if used with care they can provide additional data that may help with a research question and that may not be obtainable in any other way.
Instrumental variables
The major limitation in inferring causation from natural experiments is the presence of unmeasured confounding. One class of methods designed to control confounding and measurement error is based on instrumental variables (IV). While useful in a variety of applications, the validity and interpretation of IV estimates depend on strong assumptions, the plausibility of which must be considered with regard to the causal relation in question.
In particular, IV analyses depend on the assumption that subjects were effectively randomized, even if the randomization was accidental (in the case of an administrative policy change or exposure to a natural disaster) and adherence to random assignment was low. IV methods can be used to control for confounding in observational studies, to control for confounding due to noncompliance, and to correct for misclassification.
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IV analysis, however, can produce serious biases in effect estimates. It can also be difficult to identify the particular subpopulation to which the causal effect IV estimate applies. Moreover, IV analysis can add considerable imprecision to causal effect estimates. Small sample size poses an additional challenge in applying IV methods.