Causal decomposition of Black-white disparities in NIH’s selection of proposals for panel discussion
5 affiliations
- 1Department of Statistics, University of Washington, Seattle, Washington, United States of America.
- 2Department of Philosophy, University of Washington, Seattle, Washington, United States of America.
- 3Center for an Informed Public, University of Washington, Seattle, Washington, United States of America.
- 4School of Social Work, University of Washington, Seattle, Washington, United States of America.
- 5Center for Statistics and the Social Sciences, University of Washington, Seattle, Washington, United States of America.
Abstract
Prior research on NIH R01 grants peer review found that selection of proposals for panel discussion constituted the decision point with the largest contribution to Black-white NIH funding disparities. We use a causal decomposition framework to understand what factors causally contribute to reducing Black–white disparities in selection into discussion, and to assess how recent procedural changes may affect Black–white disparities in selection into discussion. We first use causal decomposition methodology to explore whether hypothetical interventions eliminating Black-white disparities and/or differences in attributes would reduce Black-white disparities in selection of proposals for panel discussion. The attributes we consider include those associated with applicants (degree, career stage, their institution’s funding bin) and their submitted proposals (application type, amended status, Preliminary Overall Impact Score, and NIH assigned Administering Organization and Integrated Review Group). Under reasonable assumptions, we find that among the attributes considered, Preliminary Overall Impact Score was the only one that, after equalizing, could dissolve Black-white disparities in selection into discussion. In addition, we conduct a thought experiment to explore potential impacts of the NIH’s move to binary scoring for the Investigator and Environment criteria in the new Simplified Peer Review Framework on Black-white disparities in selection into discussion. Analyses from our thought experiment suggest that changing to binary scoring of the Investigator-and-Environment factor may not have any effect on disparities in selection into discussion. Overall, under the assumptions of the causal decomposition framework, these results suggest that efforts aimed at reducing disparities in selection into discussion should focus on identifying upstream, policy-relevant, and practically feasible interventions for eliminating racial disparities in Preliminary Overall Impact Scores. In contrast, the transition to the Simplified Peer Review Framework is unlikely to substantially reduce disparities in selection into discussion.
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BibTeX
@article{prakash2026causal,
title = {{Causal decomposition of Black-white disparities in NIH’s selection of proposals for panel discussion}},
author = {Shreya Prakash and Carole J Lee and Elena A Erosheva},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0358838},
url = {https://doi.org/10.1371/journal.pone.0358838},
note = {CC BY},
}
RIS
TY - JOUR
TI - Causal decomposition of Black-white disparities in NIH’s selection of proposals for panel discussion
AU - Shreya Prakash
AU - Carole J Lee
AU - Elena A Erosheva
PY - 2026
DA - 2026/10/28
JO - PLOS One
DO - 10.1371/journal.pone.0358838
UR - https://doi.org/10.1371/journal.pone.0358838
AB - Prior research on NIH R01 grants peer review found that selection of proposals for panel discussion constituted the decision point with the largest contribution to Black-white NIH funding disparities. We use a causal decomposition framework to understand what factors causally contribute to reducing Black–white disparities in selection into discussion, and to assess how recent procedural changes may affect Black–white disparities in selection into discussion. We first use causal decomposition methodology to explore whether hypothetical interventions eliminating Black-white disparities and/or differences in attributes would reduce Black-white disparities in selection of proposals for panel discussion. The attributes we consider include those associated with applicants (degree, career stage, their institution’s funding bin) and their submitted proposals (application type, amended status, Preliminary Overall Impact Score, and NIH assigned Administering Organization and Integrated Review Group). Under reasonable assumptions, we find that among the attributes considered, Preliminary Overall Impact Score was the only one that, after equalizing, could dissolve Black-white disparities in selection into discussion. In addition, we conduct a thought experiment to explore potential impacts of the NIH’s move to binary scoring for the Investigator and Environment criteria in the new Simplified Peer Review Framework on Black-white disparities in selection into discussion. Analyses from our thought experiment suggest that changing to binary scoring of the Investigator-and-Environment factor may not have any effect on disparities in selection into discussion. Overall, under the assumptions of the causal decomposition framework, these results suggest that efforts aimed at reducing disparities in selection into discussion should focus on identifying upstream, policy-relevant, and practically feasible interventions for eliminating racial disparities in Preliminary Overall Impact Scores. In contrast, the transition to the Simplified Peer Review Framework is unlikely to substantially reduce disparities in selection into discussion.
SN - 1932-6203
ER -