Challenges and Opportunities for Causal Inference in Molecular Epidemiology

Jonathan Huang and Brian Whitcomb

While no definitive, go-to pedagogical text yet exists to teach the ins-and-out of causal inference in molecular epidemiology, we’ve put together a list of 7 papers we think address critical aspects of both the promise and challenges present in this task.  

The first three (Mehta, et al; Yang et al; Gadbury, et al) present an overview of the basic challenge of causal inference in the high-dimensional / -omic wide space, notably focusing on the statistical inferential problems that exist even in experimental settings. Underlying this is a recognized need to evaluate methods using scalable techniques that respect the complex structures and size of genomic data, notably “plasmode” base simulation. Read more