  Evaluation of treatment effects and more general estimands is typically
achieved via parametric modelling, which is unsatisfactory since model
misspecification is likely. Data-adaptive model building (e.g.
statistical/machine learning) is commonly employed to reduce the risk of
misspecification. Naive use of such methods, however, delivers estimators whose
bias may shrink too slowly with sample size for inferential methods to perform
well, including those based on the bootstrap. Bias arises because standard
data-adaptive methods are tuned towards minimal prediction error as opposed to
e.g. minimal MSE in the estimator. This may cause excess variability that is
difficult to acknowledge, due to the complexity of such strategies.
  Building on results from non-parametric statistics, targeted learning and
debiased machine learning overcome these problems by constructing estimators
using the estimand's efficient influence function under the non-parametric
model. These increasingly popular methodologies typically assume that the
efficient influence function is given, or that the reader is familiar with its
derivation.
  In this paper, we focus on derivation of the efficient influence function and
explain how it may be used to construct statistical/machine-learning-based
estimators. We discuss the requisite conditions for these estimators to perform
well and use diverse examples to convey the broad applicability of the theory.
