ReviewAnnual review of public health2026
Causal Inference in Health Disparities Research.
Review in Annual review of public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Causal inference is a central endeavor in health disparities research. For decades, it has been used to both measure and explain disparity and discrimination and to evaluate the impact of interventions on disparity and discrimination. This article reviews the use of causal inference methods for each of these endeavors, highlighting critical challenges that emerging work attempts to overcome. A key feature of a newer proposal is to use a descriptive measure of disparity that builds in normative and ethical assumptions and then to perform causal inference on that measure of disparity when seeking to inform and evaluate interventions. In this way, the measure of disparity is applicable to real-world data; is consistent across measurement, intervention development, and evaluation efforts; and builds in normative and ethical assumptions that promote transparency, dialogue, debate, and reproducibility. The article also briefly highlights causal inference methods for transformative interventions.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.