Experimental designs for identifying causal mechanisms

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 several different experimental designs that help identify average natural indirect effects. Some of these designs require the perfect manipulation of an intermediate variable, while others can be used even when only imper-fect manipulation is possible. We use recent social science experiments to illustrate the key ideas that underlie each of the proposed designs. Key Words: causal inference, direct and indirect effects, identification, instrumental variables, medi-ation ∗Replication materials for this particle are available online as Imai et al. (2011a). We thank Erin Hartman, Adam Glynn, and Erik Snowberg as well as seminar participants at Columbia University (Political Science), the University of Californi