Experimentation is a powerful methodology that enables scientists to
empirically establish causal claims. However, one important
criticism is that experiments merely provide a black-box view of
causality and fail to identify causal mechanisms. Specifically,
critics argue that although experiments can identify average causal
effects, they cannot explain the process through which such effects
come about. If true, this represents a serious limitation of
experimentation, especially for social and medical science research
that strive to identify causal mechanisms. In this paper, we
consider the several different experimental designs that help
identify average natural indirect effects. Some of these designs
require the direct manipulation of an intermediate variable, while
others can be used even when only imperfect manipulation is
possible. We use recent social science experiments to illustrate
the key ideas that underlie each of the proposed designs.
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