Abstract
Exposure-response curves are among the most widely used tools of quantitative health risk assessment. However, we propose that exactly what they mean is usually left ambiguous, making it impossible to answer such fundamental questions as whether and by how much reducing exposure by a stated amount would change average population risks and distributions of individual risks. Recent concepts and computational methods from causal artificial intelligence (CAI) and machine learning (ML) can be applied to clarify what an exposure-response curve means; what other variables are held fixed (and at what levels) in estimating it; and how much inter-individual variability there is around population average exposure-response curves. These advances in conceptual clarity and practical computational methods not only enable epidemiologists and risk analysis practitioners to better quantify population and individual exposure-response curves but also challenge them to specify exactly what exposure-response relationships they seek to quantify and communicate to risk managers and how to use the resulting information to improve risk management decisions.
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