  We introduce an algorithm that simplifies the construction of efficient
estimators, making them accessible to a broader audience. 'Dimple' takes as
input computer code representing a parameter of interest and outputs an
efficient estimator. Unlike standard approaches, it does not require users to
derive a functional derivative known as the efficient influence function.
Dimple avoids this task by applying automatic differentiation to the
statistical functional of interest. Doing so requires expressing this
functional as a composition of primitives satisfying a novel differentiability
condition. Dimple also uses this composition to determine the nuisances it must
estimate. In software, primitives can be implemented independently of one
another and reused across different estimation problems. We provide a
proof-of-concept Python implementation and showcase through examples how it
allows users to go from parameter specification to efficient estimation with
just a few lines of code.
