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In Pursuit of Evidence in Air Pollution Epidemiology: The Role of Causally Driven Data Science.

T impact of air pollution on health gained widespread attention in 1952 when pollution from coal burning in London, with smoke so thick that drivers needed headlights at all times of day, was linked to a dramatic rise in the number of deaths.1,2 Over the past decades, evidence of adverse population health effects has accumulated from many thousands of epidemiologic studies, suggesting this is a critically important public health problem. While at the individual level the relative effects of exposure tend to be small, and potentially large confounding biases are difficult to rule out, air pollution is ubiquitous and the exposed population is enormous. Thus, these adverse effects have important policy implications and intense scrutiny of these epidemiologic findings is inevitable.3,4 Recently, the emergence onto the statistics landscape of modern causal inference methods, as well as machine learning and other novel data science techniques, has generated excitement and a sense of promise that estimation of the causal effects of air pollution exposures on health outcomes could be more definitively described.5–7 (By causal effects we mean the effects that would be seen under experimental changes of exposures. By causal inference we mean the process of inferring causal effects from data.) We share the excitement of others that the discipline of causal inference has the potential to advance air pollution policy and allow the integration of modern statistical tools into air pollution epidemiology, but we also caution against unrealistic expectations by highlighting important difficulties ahead. Our goal is to provide a glimpse of the opportunities afforded by the use of causal inference and data science methods, raise awareness about some of the outstanding challenges, and inspire others to join on the efforts to overcome them. In our note, we first discuss broad concepts that are relevant across applications, and then focus issues germane to air pollution epidemiology.