Integrating Hill’s classical considerations with modern causal inference methods in observational studies: a ‘How-Questions’ framework

Abstract

Abstract Context Modern causal inference methods – although core to epidemiological reasoning – may be difficult to master and less intuitive than Hill’s classical considerations. We developed a ‘How-Questions’ (HQ) framework to integrate Hill's classical considerations with modern causal inference methods in observational studies. Methods First, we extracted the main causal considerations from contemporary philosophy of science: characteristics of empirical associations, universality, depth, and degree of corroboration of a theory. From these, we developed a HQ framework based on six domains formulated as questions: (1) how valid?, (2) how time-ordered?, (3) how big?, (4) how shaped?, (5) how replicable?, and (6) how explainable? Then, we qualitatively checked whether Hill's classical considerations and key selected modern causal inference methods were compatible with the HQ framework. Lastly, as a proof-of-concept, we applied the HQ framework to two observational studies of current topics in epidemiology. Findings Both Hill’s considerations and key selected modern causal inference methods were compatible with the six domains of the HQ framework. (1) The how-valid domain is addressed by considering the same internal validity issues in Hill’s and modern methods, namely confounding, selection and measurement biases; modern methods use more formalized techniques, including quantitative bias analyses/sensitivity analyses (QBA/SA). (2) The how-time-ordered domain is addressed by considering reverse causation in Hill’s; modern methods may use G methods within the context of longitudinal data analyses and time-varying exposures. (3) The how-big domain is addressed by strength of association in Hill’s; modern methods first consider estimands and may use QBA/SA to assess robustness of effect estimates. (4) The how-shaped domain is represented by biological gradient in Hill’s; modern methods may use generalized propensity scores to estimate dose-response functions. (5) The how-replicable domain is addressed in Hill’s by consistency of study findings with existing evidence; modern methods may use triangulation of different study designs and consider generalizability and transportability concepts. (6) The how-explainable domain is addressed by biological plausibility in Hill’s and by mediation/interaction analyses in modern methods. The application of the HQ framework to two observational studies provides a proof-of-concept and suggests its potential usefulness to integrate Hill’s considerations with modern causal inference methods. Perspective We found that the six dimensions of the HQ framework integrated Hill’s classical considerations with modern causal inference methods for observational studies. Apart from its potential pedagogical value, the HQ framework may provide a holistic view for the causal assessment of observational studies in epidemiology.