Experimental designs for identifying causal mechanisms
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 c...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Wiley Blackwell
2014
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| Online Access: | http://hdl.handle.net/1721.1/85870 https://orcid.org/0000-0002-8079-7675 |
| _version_ | 1811091809841971200 |
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| author | Imai, Kosuke Tingley, Dustin Yamamoto, Teppei |
| author2 | Massachusetts Institute of Technology. Department of Political Science |
| author_facet | Massachusetts Institute of Technology. Department of Political Science Imai, Kosuke Tingley, Dustin Yamamoto, Teppei |
| author_sort | Imai, Kosuke |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:08:23Z |
| format | Article |
| id | mit-1721.1/85870 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:08:23Z |
| publishDate | 2014 |
| publisher | Wiley Blackwell |
| record_format | dspace |
| spelling | mit-1721.1/858702022-09-29T12:56:39Z Experimental designs for identifying causal mechanisms Imai, Kosuke Tingley, Dustin Yamamoto, Teppei Massachusetts Institute of Technology. Department of Political Science Yamamoto, Teppei Yamamoto, Teppei 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. National Science Foundation (U.S.) (Grant SES-0849715) National Science Foundation (U.S.) (Grant SES-0918968) 2014-03-21T15:14:11Z 2014-03-21T15:14:11Z 2012-11 Article http://purl.org/eprint/type/JournalArticle 09641998 1467-985X http://hdl.handle.net/1721.1/85870 Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto. “Experimental Designs for Identifying Causal Mechanisms.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 176, no. 1 (January 2013): 5–51. https://orcid.org/0000-0002-8079-7675 en_US http://dx.doi.org/10.1111/j.1467-985X.2012.01032.x Journal of the Royal Statistical Society: Series A (Statistics in Society) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Wiley Blackwell Yamamoto via Jennifer Greenleaf |
| spellingShingle | Imai, Kosuke Tingley, Dustin Yamamoto, Teppei Experimental designs for identifying causal mechanisms |
| title | Experimental designs for identifying causal mechanisms |
| title_full | Experimental designs for identifying causal mechanisms |
| title_fullStr | Experimental designs for identifying causal mechanisms |
| title_full_unstemmed | Experimental designs for identifying causal mechanisms |
| title_short | Experimental designs for identifying causal mechanisms |
| title_sort | experimental designs for identifying causal mechanisms |
| url | http://hdl.handle.net/1721.1/85870 https://orcid.org/0000-0002-8079-7675 |
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