print.linearcausalproblem

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>.

Michael Sachs

causaloptim

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

Michael C Sachs

Erin E Gabriel

Arvid Sjölander

Gustav Jonzon

Alexander A Balke

Colorado Reed

print.linearcausalproblem function

<dl><dt>x</dt>
<dd>object of class "linearcausaloptim"</dd>
<dt>...</dt>
<dd>Not used</dd></dl>

Arguments

Print the causal problem — print.linearcausalproblem

<dl>

<dt>x</dt>
<dd>object of class "linearcausaloptim"</dd>


<dt>...</dt>
<dd>Not used</dd>

</dl>

Print the causal problem

print.linearcausalproblem: Print the causal problem

Description

Usage

Value

Arguments