causaloptim: An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a preprint by Sachs, Gabriel, and Sjölander (2021) <https://sachsmc.github.io/causaloptim/articles/CausalBoundsMethods.pdf>.

Package details

AuthorMichael C Sachs [aut, cre], Erin E Gabriel [aut], Arvid Sjölander [aut], Gustav Jonzon [ctb] ((improved vertex enumeration)), Alexander A Balke [ctb] ((C++ code)), Colorado Reed [ctb] ((graph-creator.js))
MaintainerMichael C Sachs <sachsmc@gmail.com>
LicenseMIT + file LICENSE
Version0.9.2
URL https://github.com/sachsmc/causaloptim
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("causaloptim")

Try the causaloptim package in your browser

Any scripts or data that you put into this service are public.

causaloptim documentation built on March 26, 2022, 1:08 a.m.