Metadata-Version: 2.1
Name: optimaladj
Version: 0.0.1
Summary: A package to compute optimal adjustment sets in causal graphs
Home-page: https://github.com/facusapienza21/optimaladj
Author: Facundo Sapienza, Ezequiel Smucler
Author-email: ezequiels.90@gmail.com
License: UNKNOWN
Description: # optimaladj: A library for computing optimal adjustment sets in causal graphical models
        
        This package implements the algorithms introduced in [Smucler, Sapienza and Rotnitzky (2020)](https://arxiv.org/abs/2004.10521) to compute optimal adjustment sets in causal graphical models.
        The package provides a class, called CasualGraph, that inherits from [networkx's](https://networkx.org/) DiGraph class and has methods
        to compute: the optimal, optimal minimal and optimal minimum adjustment sets for individualized
        treatment rules (point exposure dynamic treatment regimes) in non-parametric causal graphical
        models with latent variables. 
        
        Suppose we are given a causal graph G specifying:
        
        * a treatment variable A,
        * an outcome variable Y,
        * a set of observable (that is, non-latent) variables N, and
        * a set of observable variables that will be used to allocate treatment L.
        
        Suppose moreover that there exists at least one adjustment set with respect to A and Y in G that is comprised of observable variables.
        
        An optimal adjustment set is an observable adjustment set that yields the non-parametric estimator of the interventional mean with the smallest asymptotic variance among those that are based on observable adjustment sets. 
        
        An optimal minimal adjustment set is an observable adjustment set that yields the non-parametric estimator of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal adjustment sets. An observable minimal adjustment set is a valid adjustment set such that all its variables are observable and the removal of any variable from it destroys validity.
        
        An optimal minimum adjustment set is defined similarly, being optimal in the class of observable minimum adjustment sets, that is, valid observable adjustment sets of minimum cardinality among the observable adjustment sets.
        
        Under these assumptions, [Smucler, Sapienza and Rotnitzky (2020)](https://arxiv.org/abs/2004.10521) show that 
        optimal minimal and optimal minimum adjustment sets always exist, and can be computed in polynomial time. They also provide a sufficient criterion for the existance of an optimal adjustment set and a polynomial time algorithm to compute it when it exists.
        
        Check out our notebook with [examples](https://github.com/facusapienza21/optimaladj/blob/main/examples/Examples.ipynb).
        
        ## Installation
        
        You can install the development version of the package from Github by running
        
        ```sh
        pip install git+https://github.com/facusapienza21/optimaladj.git#egg=optimaladj
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
