Computational stemmatology is the discipline dealing with the reconstruction of the copy history of historical works, primarily transmitted by handwriting. The result is a directed acyclic graph or tree. Trees generated by computational means are often unrooted. While in biology, all lifeforms are connected in a huge tree of life (compare tolweb.org), for texts such a tree seems an unsuitable metaphor. For this reason, rooting a tree in biology is relatively easy while it is relatively hard in stemmatology. Biologists in the majority of cases use a so-called outgroup, that is a species remotely - but not too remotely - related to the group under investigation. Then they identify root that node which is closest to the outgroup since the outgroup is considered to indicate where the group connects with the tree of life. There are some alternative rooting procedures none of which is applicable to stemmatology generally. Haigh (1970, 1971) proposes to assign root according to probabilities assuming a certain type of birth-process as a generating function for stemmata but immediately relativises his proposal saying that the process used is not historically realistic. Yet other approaches are pursued by Marmerola et al. (2016). We implement the approach of Haigh (1970), one of the approaches of Marmerola et al. (2016) (called minimum-cost heuristic) and present our own two approaches which we then test on 3 artificial stemmatological datasets.