ArticleJournal of the American Statistical Association2020
Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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.
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Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Assessing the Evidence for Differential Health Effects of Wildfire Smoke Across Fires and Populations: A Critical Review of Recent Studies.Current environmental health reports · 2026Review
- Impact of fog on California waterfowl flight activity: historical and modern insights into effects post-Clean Air Act.Environmental science and pollution research international · 2026Article
- Air Pollution and Cardiac Remodeling and Function in Patients With Breast Cancer.JAMA network open · 2026Article
- Robust Statistical Approaches to Understanding the Causal Effect of Air Pollution Mixtures.Research report (Health Effects Institute) · 2025Article
- Global, regional, and national burden of young COPD, 1990-2021, with forecasts to 2050: a systematic analysis for the global burden of disease study 2021.BMC public health · 2025Article
- Global burden of pulmonary sarcoidosis from 1990 to 2021: a comprehensive analysis based on the GBD 2021 study.Frontiers in medicine · 2025Article
- Effects of major air pollutants on cognitive function in middle-aged and elderly adults: Panel data evidence from China Health and Retirement Longitudinal Study.Journal of global health · 2024Article
- Causal analysis of air pollution mixtures: estimands, positivity, and extrapolation.American journal of epidemiology · 2024Article
- Association of Air Quality Improvement and Frailty Progression: A National Study across China.Toxics · 2024Article
- An integrated analysis of air pollution and meteorological conditions in Jakarta.Scientific reports · 2023Article
- Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.Journal of agricultural, biological, and environmental statistics · 2023Article
- Methods for Evaluating Environmental Health Impacts at Different Stages of the Policy Process in Cities.Current environmental health reports · 2022Review
- 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
- Scaling the Superblock model to city level in Barcelona?: Learning from recent policy impact evaluations.Contesti : citta territori progetti · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the Total Events Avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, utilizing population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the US Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure.
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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.