Social scientists increasingly exploit natural experiments in their research. This article surveys recent applications in political science, with the goal of illustrating the inferential advantages provided by this research design. When treatment assignment is less than "as if" random, studies may be something less than natural experiments, and familiar threats to valid causal inference in observational settings can arise. The author proposes a continuum of plausibility for natural experiments, defined by the extent to which treatment assignment is plausibly "as if" random, and locates several leading studies along this continuum.
Keywords: natural experiment; "as if" random; exogenous variation; continuum of plausibility; matching
If I had any desire to lead a life of indolent ease, I would wish to be an identical twin, separated at birth from my brother and raised in a different social class. We could hire ourselves out to a host of social scientists and practically name our fee. For we would be exceedingly rare representatives of the only really adequate natural experiment for separating genetic from environmental effects in humans-genetically identical individuals raised in disparate environments.
-Stephen Jay Gould (1996, 264)
Social scientists are increasingly exploiting natural experiments in their research. A recent search on "natural experiment" using "Google Scholar" (scholar .google.com) turned up more than 1 million hits; the results appearing on the first dozen pages suggest that economics and epidemiology are the leading fields to use the term, but political science is also well represented. An impressive volume of unpublished, forthcoming, and recently published studies in political science suggests the growing influence of the natural experimental approach. Table 1 provides a nonexhaustive list of several recent studies.
As the name suggests, natural experiments take their inspiration from the experimental approach. A randomized controlled experiment (Freedman, Pisani, and Purves 1997, 4-8) has three hallmarks. First, the response of experimental subjects to a "treatment" (or a series of treatments) is compared to the response of other subjects to a "control" regime, often defined as the absence of a treatment. Second, the assignment of subjects to treatment and control groups is done at random. Third, the application or manipulation of the treatment is under the control of the experimental researcher. Each of these traits plays a critical role in the experimental model of causal inference. For example, in a medical trial of a new drug, the fact that subjects in the treatment group take the drug, while those in the control group do not, allows for a comparison of health outcomes across the two groups. Random assignment ensures that any difference in average outcomes between the two groups is not due to confounders, or factors other than the treatment that vary across the two groups and that may explain differences in health outcomes. Finally, experimental manipulation of the treatment establishes evidence for a causal relationship between the treatment and the health outcomes.1
Unlike true experiments, the data used in natural experiments come from naturally occurring phenomena-actually, in the social sciences, from phenomena that are often the product of social and political forces. Because the manipulation of treatment variables is not generally under the control of the analyst, natural experiments are, in fact, observational studies. However, unlike other nonexperimental approaches, a researcher exploiting a natural experiment can make a credible claim that the assignment of the nonexperimental subjects to treatment and control conditions is "as if" random. Outcomes are compared across treatment and control groups, and both a priori reasoning and empirical evidence are used to validate the assertion of randomization. Thus, random or "as if" random of assignment to treatment and control conditions constitutes the defining feature of a natural experiment. …