update_effect

If you want to use the same graph and response function, but change the 
effect of interest, this can save some computation time.

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

update_effect function

<dl><dt>obj</dt>
<dd>An object as returned by analyze_graph</dd>
<dt>effectt</dt>
<dd>A character string that represents the causal effect of interest</dd></dl>

Arguments

Update the effect in a linearcausalproblem object — update_effect

<dl>

<dt>obj</dt>
<dd>An object as returned by analyze_graph</dd>


<dt>effectt</dt>
<dd>A character string that represents the causal effect of interest</dd>

</dl>

update_effect: Update the effect in a linearcausalproblem object

Description

Usage

Value

Arguments