  Strong untestable assumptions are almost universal in causal point
estimation. In particular settings, bounds can be derived to narrow the
possible range of a causal effect. Symbolic bounds apply to all settings that
can be depicted using the same directed acyclic graph (DAG) and for the same
effect of interest. Although the core of the methodology for deriving symbolic
bounds has been previously developed, the means of implementation and
computation have been lacking. Our R-package causaloptim aims to solve this
usability problem by implementing the method of Sachs et al. (2022a) and
providing the user with a graphical interface through shiny that allows for
input in a way that most researchers with an interest in causal inference will
be familiar; a DAG (via a point-and-click experience) and specifying a causal
effect of interest using familiar counterfactual notation.
