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 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 average difference in 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 condition 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 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 non-experimental approaches, a researcher
exploiting a natural experiment can make a credible claim that the assignment of the non-
experimental 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. It is also what distinguishes the approach from what
1
For a discussion of “manipulationist” accounts of causation, see Goldthorpe (2001) and Brady (2002).
Convention