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/B9781558603325.500110> 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 


Author  Michael 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] ((graphcreator.js)) 
Maintainer  Michael C Sachs <sachsmc@gmail.com> 
License  MIT + file LICENSE 
Version  0.9.2 
URL  https://github.com/sachsmc/causaloptim 
Package repository  View on CRAN 
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