An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects
An Interface to Specify Causal Graphs and Compute Bounds on Causal Eff...
Check conditions on causal problem
Check constraints
Analyze the causal graph and effect to determine constraints and objec...
Shiny interface to specify network structure and compute bounds
Update the effect in a linearcausalproblem object
Translate target effect to vector of response variables
Translate response functions into matrix of counterfactuals
Create constraint matrix
Translate regular DAG to response functions
Define default effect for a given graph
Check conditions on digraph
Convert bounds string to a function
Latex bounds equations
Compute a bound on the average causal effect
Run the Balke optimizer
Run the optimizer
Parse text that defines a the constraints
Parse text that defines a causal effect
Plot the graph from the causal problem with a legend describing attrib...
Plot the analyzed graph object
Print the causal problem
Check conditions on query
Simulate bounds
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 paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.