The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
mediation:R Package for Causal Mediation Analysis
Open accessAbstract
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
Preacher, K. J., & Hayes, A. F. (2008) · Behavior Research Methods · doi:10.3758/brm.40.3.879 · Cited by 34,117
Introduction to Statistical Mediation Analysis
MacKinnon, D. P. (2012) · doi:10.4324/9780203809556 · Cited by 5,618
A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
Self-citationBootstrap confidence intervals
DiCiccio, T. J., & Efron, B. (1996) · Statistical Science · doi:10.1214/ss/1032280214 · Cited by 2,286
The Statistical Implications of a System of Simultaneous Equations
Haavelmo, T. (1943) · Econometrica · doi:10.2307/1905714 · Cited by 966
Interpretation and identification of causal mediation
Pearl, J. (2014) · Psychological Methods · doi:10.1037/a0036434 · Cited by 554
Experimental Designs for Identifying Causal Mechanisms
Imai, K., Tingley, D., & Yamamoto, T. (2013) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2012.01032.x · Cited by 408
Self-citation
Cited by
Items 101–103 of 103
Learning by correcting AI errors: effort is essential
Olney, A. M., & Cade, W. L. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-98459-4_11 · Cited by 2
Valadez, E. A., Jopling, E., Macera, E., Wade, M., Zeanah, C. H., Nelson, C. A., & Fox, N. A. (2026) · Development and Psychopathology · doi:10.1017/s0954579426101928
Fang, C. M., Liu, A. R., Danry, V., Lee, E., Chan, S. W. T., Pataranutaporn, P., Maes, P., Phang, J., Lampe, M., Ahmad, L., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.17473 · Cited by 109
Metadata
- Authors
- Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2014
- Volume
- 59
- Issue
- 5
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W1908991493
- Cited by
- 4,155
- References
- 8
- FWCI
- 98.30
- Type
- journal article
- Language
- en
- Open access
- diamond
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.