Study finds strong evidence for a causal link between long-term exposure to fine air particles and greater mortality in elderly Americans

A new analysis of 16 years of publicly accessible health data on 68.5 million Medicare enrollees provides broad evidence that long-term exposure to fine particles in the air – even at levels below current EPA standards – leads to increased mortality rates among the elderly. Based on the results of five complementary statistical models, including three causal inference methods, the researchers estimate that if the EPA had lowered the air quality standard for fine particle concentration from 12 μg/m3 down to the WHO guideline of 10 μg/m3, more than 140,000 lives might have been saved within one decade. “Our findings provide the strongest evidence to date that current national air quality standards aren’t sufficiently protective of Americans’ health,” said corresponding author Francesca Dominici. “Now, in the middle of a pandemic that attacks our lungs and makes us unable to breathe, it is irresponsible to roll back environmental policies,” she added. The new study is likely to inform national discussions around updating air quality standards, for example, the National Ambient Air Quality Standards by the EPA.

A number of studies have documented a strong correlation between long-term exposure to fine particulate and greater human mortality, but some concern has remained about the causal nature of the evidence, and whether it is sufficient to inform revisions to air quality standards. Some scientists argue that modern causal inference methods can provide such evidence, using the right data. “Causal inference can quantify and visualize how close our data are to approximating a randomized controlled study, the gold standard for assessing causation,” said the study’s lead author Xiao Wu. Analyzing a massive dataset through five distinct approaches, including two traditional statistical methods and three causal inference methods, Wu and colleagues derived broad evidence consistent with a causal link between long-term particulate exposure and mortality.

The original article can be found here.

In the field of causal inference, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.”  In 2020, Professor Pearl is also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). In the future, Professor Judea will also contribute to Causal Inference for AI transparency, which is one of important AIWS topics on AI Ethics.