Mendelian randomization (MR) can be variously dated as 67, 1 33, 2 28 3 or 16 4 (amongst others) years old. It is clear that in recent times there has been an exponential increase in publications on MR, both theoretical and applied. 5,6 In addition to papers focused on MR, MR analyses are increasingly seen in the set of follow-up analyses for a genome-wide association study (GWAS), together with the obligatory bioinformatic and functional evidence. The Internationl Journal of Epidemiology compiled a special issue on MR to accompany the second international MR conference in 2015. 7 The present issue repeats this for the fourth international conference. The speed of progress in the field is reflected in both the content of the issues, and by the fact that the volume of material now exceeds the capacity of a single issue. Further compilations of MR papers will appear in subsequent issues. MR Made (Too?) Easy The initial extended exposition of MR in 2003 included examples of two-sample Mendelian randomization (MR), 4 but the rise in popularity of the two-sample approach came with the development of methods for MR using publicly-available summary data. 8-10 It is now increasingly easy to perform an MR analysis without personally collecting any data. This is not necessarily a negative point, as such analyses have several advantages: they are able to use large data resources for many disease outcomes, and they can be replicated by anyone having access to the same data. However, it is also increasingly easy to perform an MR analysis without any critical thought. The formula-conduct a genome-wide association study, take all genome-wide significant variants, perform a two-sample MR analysis-can certainly increase one's publication count. But can such an analysis be considered a contribution to the scientific literature, as it could have been (and probably has been 11) performed already by a machine in a large automated pipeline for large numbers of risk factors and outcomes?