An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

03/13/2013
by   Tom S. Verma, et al.
0

In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether there exists a causal model that explains ALL the observed dependencies and independencies. Formally, given a list M of conditional independence statements, it is required to decide whether there exists a directed acyclic graph (dag) D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via dseparation in D. We present and analyze an effective algorithm that tests for the existence of such a day, and produces one, if it exists.

READ FULL TEXT
03/06/2013

Deciding Morality of Graphs is NP-complete

In order to find a causal explanation for data presented in the form of ...
03/27/2013

On the Logic of Causal Models

This paper explores the role of Directed Acyclic Graphs (DAGs) as a repr...
08/26/2020

Complexity as Causal Information Integration

Complexity measures in the context of the Integrated Information Theory ...
03/13/2013

Optimizing Causal Orderings for Generating DAGs from Data

An algorithm for generating the structure of a directed acyclic graph fr...
01/10/2013

On characterizing Inclusion of Bayesian Networks

Every directed acyclic graph (DAG) over a finite non-empty set of variab...
11/16/2020

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Motivated by detection of cascades of defaults in economy, we developed ...
03/27/2013

On the Equivalence of Causal Models

Scientists often use directed acyclic graphs (days) to model the qualita...