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Experimental Designs for Identifying Causal Mechanisms
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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.
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Metadata
- Authors
- Imai, K., Tingley, D., & Yamamoto, T.
- Affiliations
- Princeton University (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-11-01
- Volume
- 176
- Issue
- 1
- Pages
- 5-51
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2133554893
- Cited by
- 408
- References
- 137
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 40 · 2023: 31 · 2022: 35 · 2021: 33 · 2020: 33 · 2019: 30 · 2018: 24 · 2017: 20 · 2016: 27 · 2015: 25 · 2014: 16 · 2013: 12 · 2012: 9
- FWCI
- 18.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Florence Research (University of Florence) (submittedVersion)
- Funders
- National Science Foundation (SES–0849715, SES–0918968)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Keywords
- causal mechanisms, experimental design, mediation, intermediate variables, causal inference, social science experiments
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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