Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach im...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Oxford University Press
2014
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| Online Access: | http://hdl.handle.net/1721.1/85869 https://orcid.org/0000-0002-8079-7675 |
| _version_ | 1826191837383098368 |
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| author | Yamamoto, Teppei Imai, Kosuke |
| author2 | Massachusetts Institute of Technology. Department of Political Science |
| author_facet | Massachusetts Institute of Technology. Department of Political Science Yamamoto, Teppei Imai, Kosuke |
| author_sort | Yamamoto, Teppei |
| collection | MIT |
| description | Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation. |
| first_indexed | 2024-09-23T09:02:03Z |
| format | Article |
| id | mit-1721.1/85869 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:02:03Z |
| publishDate | 2014 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | mit-1721.1/858692022-09-30T12:55:54Z Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments Yamamoto, Teppei Imai, Kosuke Massachusetts Institute of Technology. Department of Political Science Yamamoto, Teppei Yamamoto, Teppei Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation. National Science Foundation (U.S.) (SES-0918968) 2014-03-21T15:07:37Z 2014-03-21T15:07:37Z 2013-01 Article http://purl.org/eprint/type/JournalArticle 1047-1987 1476-4989 http://hdl.handle.net/1721.1/85869 Imai, K., and T. Yamamoto. “Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments.” Political Analysis 21, no. 2 (April 1, 2013): 141–171. https://orcid.org/0000-0002-8079-7675 en_US http://dx.doi.org/10.1093/pan/mps040 Political Analysis Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Oxford University Press Yamamoto via Jennifer Greenleaf |
| spellingShingle | Yamamoto, Teppei Imai, Kosuke Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title_full | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title_fullStr | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title_full_unstemmed | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title_short | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments |
| title_sort | identification and sensitivity analysis for multiple causal mechanisms revisiting evidence from framing experiments |
| url | http://hdl.handle.net/1721.1/85869 https://orcid.org/0000-0002-8079-7675 |
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