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Improving Causal Inference: Strengths and Limitations of Natural Experiments
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Abstract
Social scientists increasingly exploit natural experiments in their research. Unlike true experiments, natural experiments are observational studies in which the researcher does not manipulate the political and social world to introduce a “treatment” and a “control.” However, unlike other observational studies, in a natural experiment the researcher claims that the assignment of subjects to treatment and control groups is random or “as if” random. This paper surveys recent applications in political science, with the goal both of illustrating the utility of natural experiments in a variety of substantive contexts and of delineating the sorts of inferential issues over which natural experiments may offer analysts less leverage. 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. Even in natural experiments that exploit a true randomizing device, the leverage provided by natural experiments can sometimes be limited by both internal and external validity concerns. These issues are quite common and, as I show, they may restrict the usefulness of the widespread practice of exploiting natural experiments as a source of instrumental variables in regression analyses.
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Abstract
Social scientists increasingly exploit natural experiments in their research. Unlike true
experiments, natural experiments are observational studies in which the researcher does
not manipulate the political and social world to introduce a “treatment” and a “control.”
However, unlike other observational studies, in a natural experiment the researcher
claims that the assignment of subjects to treatment and control groups is random or “as
if” random. This paper surveys recent applications in political science, with the goal both
of illustrating the utility of natural experiments in a variety of substantive contexts and of
delineating the sorts of inferential issues over which natural experiments may offer
analysts less leverage. 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. Even in natural experiments that exploit a
true randomizing device, the leverage provided by natural experiments can sometimes be
limited by both internal and external validity concerns. These issues are quite common
and, as I show, they may restrict the usefulness of the widespread practice of exploiting
natural experiments as a source of instrumental variables in regression analyses.
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