  The literature comparing the performance of algorithms for assigning cause of
death using verbal autopsy data is fractious and does not reach a consensus on
which algorithms perform best, or even how to do the comparison. This
manuscript explains the challenges and suggests a way forward. A universal
challenge is the lack of standard training and testing data. This limits
meaningful comparisons between algorithms, and further, limits the ability of
any algorithm to classify verbal autopsy deaths by cause in a way that is
widely generalizable across regions and through time. Verbal autopsy algorithms
utilize a variety of information to describe the relationship between verbal
autopsy symptoms and causes of death - called symptom-cause information (SCI).
A crowd sourced, public archive of SCI managed by the World Health Organization
(WHO) is suggested as a way to address the lack of SCI for developing, testing,
and comparing verbal autopsy coding algorithms, and additionally, as a way to
ensure that algorithm-assigned causes of death are as accurate and comparable
across regions and through time as possible.
