Cited by 4,143Advanced Causal Inference TechniquesStatistical Methods and Bayesian InferencePhilosophy of Biology and Scientific Explanation

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A general approach to causal mediation analysis.

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Imai, Kosuke, Keele, Luke, Tingley, Dustin·2010·Psychological Methods

DOI 10.1037/a0020761

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Abstract

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods.

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Authors: Imai, K., Keele, L., & Tingley, D.
Affiliations: Princeton University (US)
The Ohio State University (US)
Harvard University (US)
ORCID: Imai, Kosuke
Keele, Luke
Tingley, Dustin
Venue: Psychological Methods
Publisher: American Psychological Association (APA)
Published: 2010
Volume: 15
Issue: 4
Pages: 309-334
ISSN: 1082-989X (ISSN-L)
1939-1463
DOI: 10.1037/a0020761
OpenAlex: W2010143351
Cited by: 4,143
References: 65
Citations by year: 2026: 736 · 2025: 494 · 2024: 385 · 2023: 352 · 2022: 353 · 2021: 327 · 2020: 317 · 2019: 232 · 2018: 218 · 2017: 186 · 2016: 146 · 2015: 129 · 2014: 110 · 2013: 64 · 2012: 54
FWCI: 42.48
Type: journal article
Language: en
Open access: green
nrs.harvard.edu
Licence: cc-by
Locations: Psychological Methods (publishedVersion)
PubMed (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
Funders: National Science Foundation (SES-0918968)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
Keywords: causal mediation analysis, identification assumptions, sensitivity analysis, model-free estimation
MeSH terms: Algorithms, Data Interpretation, Statistical, Sensitivity and Specificity, Social Sciences, Models, Statistical, Causality, Linear Models

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A general approach to causal mediation analysis.

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