  This paper aims to provide a tutorial for upper level undergraduate and
graduate students in statistics, biostatistics and epidemiology on deriving
influence functions for non-parametric and semi-parametric models. The author
will build on previously known efficiency theory and provide a useful identity
and formulaic technique only relying on the basics of integration which, are
self-contained in this tutorial and can be used in most any setting one might
encounter in practice. The paper provides many examples of such derivations for
well-known influence functions as well as for new parameters of interest. The
influence function remains a central object for constructing efficient
estimators for large models, such as the one-step estimator and the targeted
maximum likelihood estimator. We will not touch upon these estimators at all
but readers familiar with these estimators might find this tutorial of
particular use.
