  In this paper we give a brief review of semiparametric theory, using as a
running example the common problem of estimating an average causal effect.
Semiparametric models allow at least part of the data-generating process to be
unspecified and unrestricted, and can often yield robust estimators that
nonetheless behave similarly to those based on parametric likelihood
assumptions, e.g., fast rates of convergence to normal limiting distributions.
We discuss the basics of semiparametric theory, focusing on influence
functions.
