Commentary: Using potential outcomes causal methods to assess whether reductions in PM2.5 result in decreased mortality

Julie E Goodman; Wenchao Li; Louis Anthony Cox Jr

doi:10.1016/j.gloepi.2021.100052

Commentary: Using potential outcomes causal methods to assess whether reductions in PM_2.5 result in decreased mortality

Glob Epidemiol. 2021 Apr 2:3:100052. doi: 10.1016/j.gloepi.2021.100052. eCollection 2021 Nov.

Authors

Julie E Goodman¹, Wenchao Li¹, Louis Anthony Cox Jr^{2

3}

Affiliations

¹ Gradient, One Beacon Street, 17th Floor, Boston, MA, USA.
² Cox Associates, 503 North Franklin Street, Denver, CO, USA.
³ University of Colorado Denver, 1201 Larimer Street, Denver, CO, USA.

Abstract

Causal inference regarding exposures to ambient fine particulate matter (PM_2.5) and mortality estimated from observational studies is limited by confounding, among other factors. In light of a variety of causal inference frameworks and methods that have been developed over the past century to specifically quantify causal effects, three research teams were selected in 2016 to evaluate the causality of PM_2.5-mortality association among Medicare beneficiaries, using their own selections of causal inference methods and study designs but the same data sources. With a particular focus on controlling for unmeasured confounding, two research teams adopted an instrumental variables approach under a quasi-experiment or natural experiment study design, whereas one team adopted a structural nested mean model under the traditional cohort study design. All three research teams reported results supporting an estimated counterfactual causal relationship between ambient PM_2.5 and all-cause mortality, and their estimated causal relationships are largely of similar magnitudes to recent epidemiological studies based on regression analyses with omitted potential confounders. The causal methods used by all three research teams were built upon the potential outcomes framework. This framework has marked conceptual advantages over regression-based methods in addressing confounding and yielding unbiased estimates of average treatment effect in observational epidemiological studies. However, potential violations of the unverifiable assumptions underlying each causal method leave the results from all three studies subject to biases. We also note that the studies are not immune to some other common sources of bias, including exposure measurement errors, ecological study design, model uncertainty and specification errors, and irrelevant exposure windows, that can undermine the validity of causal inferences in observational studies. As a result, despite some apparent consistency of study results from the three research teams with the wider epidemiological literature on PM_2.5-mortality statistical associations, caution seems warranted in drawing causal conclusions from the results. A possible way forward is to improve study design and reduce dependence of conclusions on untested assumptions by complementing potential outcomes methods with structural causal modeling and information-theoretic methods that emphasize empirically tested and validated relationships.

Keywords: Causal methods; Fine particulate matter; Instrumental variable; Mortality; Potential outcomes framework; Structural nested mean model.