915Inference on Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses (ICE CRISTAL). (2nd September 2021) - Hopper, John; Li, Shuai; Bui, Minh; Dowty, James
Abstract: 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.