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Improving interventional causal predictions in regulatory risk assessment.

Louis Anthony Cox1

  • 1Cox Associates, MoirAI, Entanglement, and University of Colorado, Denver, CO, USA.

Critical Reviews in Toxicology
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PubMed
Summary

The US EPA

Area of Science:

  • Environmental health science
  • Epidemiology
  • Biostatistics

Background:

  • The US Environmental Protection Agency (EPA) published a risk assessment in 2022 on fine particulate matter (PM2.5).
  • This assessment estimated potential reductions in PM2.5-associated health risks under revised air quality standards.
  • The predictions were framed as interventional causal predictions of mortality risk reduction.

Purpose of the Study:

  • To evaluate the validity of causal predictions made in the EPA's 2022 risk assessment.
  • To examine the suitability of study designs and statistical methods used in two key EPA-selected mortality studies.
  • To assess whether the conditions for valid causal inference were met in the analyzed studies.

Main Methods:

  • Reviewed two long-term mortality studies utilized by the EPA for risk assessment.
Keywords:
CausalityPM2.5mortality riskproportional-hazards modelrisk assessment

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  • Examined the application of Cox proportional hazards (PH) models in these studies.
  • Assessed the four key conditions required for valid interventional causal predictions: study design, causal models, assumption satisfaction, and adjustment for non-causal factors.

    • Main Results:

    • The two reviewed studies, using Cox PH models, did not meet the necessary conditions for valid causal inference.
    • Key requirements for supporting interventional causal conclusions were not satisfied.
    • This raises concerns about the interpretability and validity of the EPA's causal predictions regarding PM2.5 health risks.

    Conclusions:

    • The study designs and methods used in the EPA's selected PM2.5 mortality studies are inadequate for supporting interventional causal predictions.
    • Greater rigor in study design, causal modeling, and statistical analysis is needed for credible environmental health risk assessments.
    • Stakeholders should demand that risk assessments making causal claims are based on appropriate methodologies for intervention effects.