915Inference on Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses (ICE CRISTAL)

Focus of Presentation Causation is critical if epidemiology is to be relevant to public health. Mendelian Randomisation makes causal inference about from simply observing an association between the outcome and a genetic variable that has been conferred the title of “instrumental” because the proponents consider it satisfies some assumptions perfectly. We take this association as the starting point of a Popperian approach that tries to falsify causal hypotheses by relaxing assumptions and considering alternate models. Findings We developed methods to calculate test-of-fit statistics for different causal scenarios based on the joint changes in regression coefficients, using simulations and bootstrapping methods. Let Y be the outcome, X a putative cause, and G a potential instrumental variable associated with X and Y. We regress Y against X and G alone, and with X and G together. We predicted the changes to regression coefficients that should occur under three scenarios; (i) X causes Y, (ii) there is a factor C associated with Y, X and G. and (ii) Y causes X. We compared goodness-of-fit statistics across scenarios, and for combinations of scenarios (given multiple causal processes might co-exist). We present findings from application to data on body mass index and DNA methylation and compare with Mendelian Randomisation analyses. Conclusions/Implications Robust inference can be made but the sample sizes and strengths of associations need to be substantive. Key messages Causation is a fundamentally important issue that should, and can, be addressed by trying to disprove it, rather than by finding evidence for it.