Premium
Experimental designs for identifying causal mechanisms
Author(s) -
Imai Kosuke,
Tingley Dustin,
Yamamoto Teppei
Publication year - 2013
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2012.01032.x
Subject(s) - causality (physics) , imperfect , computer science , causal model , causal inference , causation , process (computing) , criticism , key (lock) , natural (archaeology) , black box , psychology , cognitive psychology , data science , management science , artificial intelligence , epistemology , econometrics , mathematics , economics , biology , art , paleontology , philosophy , linguistics , physics , statistics , literature , computer security , quantum mechanics , operating system
Summary. Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. 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 strives to identify causal mechanisms. We consider several experimental designs that help to identify average natural indirect effects. Some of these designs require the perfect manipulation of an intermediate variable, whereas 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 designs proposed.