Objective causal predictions from observational data.

Many recent articles in public health risk assessment have stated that causal conclusions drawn from observational data must rely on inherently untestable assumptions. They claim that such assumptions ultimately can only be evaluated by informed human judgments. We call this the to causal interpretation of observational results. Its theoretical and conceptual foundation is a potential outcomes model of causation in which counterfactual outcomes cannot be observed. It risks depriving decision-makers and the public of the key benefits of traditional objective science, which invites scrutiny and independent verification through testable causal models and interventional hypotheses. We introduce an alternative to causal analysis of exposure-response relationships in observational data. This is designed to be more objective in the specific sense that it is independently verifiable (or refutable) and data-driven, requiring no inherently untestable assumptions. This approach uses empirically testable interventional causal models, specifically causal Bayesian networks (CBNs), instead of untestable potential outcomes models. It enables empirical validation of causal claims through Invariant Causal Prediction (ICP) tests across multiple studies. We explain how to use CBNs and individual conditional expectation (ICE) plots to quantify the effects on health risks of changing exposures while taking into account realistic complexities such as imperfectly controlled confounding, missing data, and measurement error. By ensuring that all causal assumptions are explicit and empirically testable, our framework may help to improve the reliability and transparency of causal inferences in health risk assessments.