  Although review papers on causal inference methods are now available, there
is a lack of introductory overviews on what they can render and on the guiding
criteria for choosing one particular method. This tutorial gives an overview in
situations where an exposure of interest is set at a chosen baseline (`point
exposure') and the target outcome arises at a later time point. We first phrase
relevant causal questions and make a case for being specific about the possible
exposure levels involved and the populations for which the question is
relevant. Using the potential outcomes framework, we describe principled
definitions of causal effects and of estimation approaches classified according
to whether they invoke the no unmeasured confounding assumption (including
outcome regression and propensity score-based methods) or an instrumental
variable with added assumptions. We discuss challenges and potential pitfalls
and illustrate application using a `simulation learner', that mimics the effect
of various breastfeeding interventions on a child's later development. This
involves a typical simulation component with generated exposure, covariate, and
outcome data that mimic those from an observational or randomised intervention
study. The simulation learner further generates various (linked) exposure types
with a set of possible values per observation unit, from which observed as well
as potential outcome data are generated. It thus provides true values of
several causal effects. R code for data generation and analysis is available on
www.ofcaus.org, where SAS and Stata code for analysis is also provided.
