ArticleThe annals of applied statistics2020
A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter.
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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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.
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Who cites it
8 citing papers in PubMed, 0 citations in OpenAlex.
- Towards a burden of disease research agenda.Population health metrics · 2026Article
- Authors' reply to the Discussion of 'Methods for estimating the exposure-response curve to inform the new safety standards for fine particulate matter'.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- A Historical Survey of Key Epidemiological Studies of Ionizing Radiation Exposure.Radiation research · 2024Review
- External exposome and all-cause mortality in European cohorts: the EXPANSE project.Frontiers in epidemiology · 2024Article
- Generalized propensity score approach to causal inference with spatial interference.Biometrics · 2023Article
- Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.Research report (Health Effects Institute) · 2022Review
- Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.Journal of the American Statistical Association · 2020Article
- Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions.Current environmental health reports · 2019Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
In the last two decades, ambient levels of air pollution have declined substantially. At the same time, the Clean Air Act mandates that the National Ambient Air Quality Standards (NAAQS) must be routinely assessed to protect populations based on the latest science. Therefore, researchers should continue to address the following question: is exposure to levels of air pollution below the NAAQS harmful to human health? Furthermore, the contentious nature surrounding environmental regulations urges us to cast this question within a causal inference framework. Several parametric and semi-parametric regression approaches have been used to estimate the exposure-response (ER) curve between long-term exposure to ambient air pollution concentrations and health outcomes. However, most of the existing approaches are not formulated within a formal framework for causal inference, adjust for the same set of potential confounders across all levels of exposure, and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment), which a) allows for different confounders
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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.