Experimental Designs for Identifying Causal Mechanisms

• Activity Number: 326
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Kosuke Imai*+ and Dustin Tingley and Teppei Yamamoto
• Companies: Princeton University and Harvard University and Princeton University
• Keywords: causal inference ; causal effects ; indirect effects ; direct effects ; identification ; causal mediation

Abstract

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. Critics argue that although experiments can identify average causal effects, they cannot explain how such effects come about. If true, this represents a serious limitation of experimentation, especially for social and medical science research whose primary goal is to identify causal mechanisms. In this paper, we consider several different experimental designs and compare their identification power. Some of these designs require the direct manipulation of mechanisms, 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 design.