An algorithm for deciding if a set of observed independencies has a causal explanation

@article{verma1992causalexplanation, abstract = {In a previous paper [8] 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 D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via d-separation in D. We present and analyze an effective algorithm that tests for the existence of such a dag, and produces one, if it exists.}, address = {San Francisco, CA, USA}, author = {Verma, Thomas and Pearl, Judea }, booktitle = {Eighth Conference on Uncertainty in Artificial intelligence}, citeulike-article-id = {2984619}, editor = {Dubois, D. and Wellman, M. P. and D'Ambrosio, B. and Smets, P. }, keywords = {causality, pdag}, location = {Stanford, CA, USA}, posted-at = {2008-07-10 11:53:02}, priority = {2}, publisher = {Morgan Kaufmann Publishers}, title = {An algorithm for deciding if a set of observed independencies has a causal explanation}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.1913}, year = {1992} }

TY - JOUR ID - verma1992causalexplanation L3 - citeulike-article-id:2984619 TI - An algorithm for deciding if a set of observed independencies has a causal explanation T2 - Eighth Conference on Uncertainty in Artificial intelligence AD - San Francisco, CA, USA CY - Stanford, CA, USA PB - Morgan Kaufmann Publishers N2 - In a previous paper [8] 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 D that is perfectly consistent with M, namely, every statement in M, and no other, is reflected via d-separation in D. We present and analyze an effective algorithm that tests for the existence of such a dag, and produces one, if it exists. KW - causality KW - pdag AU - Verma, Thomas AU - Pearl, Judea A2 - Dubois, D A2 - Wellman, MP A2 - D'Ambrosio, B A2 - Smets, P PY - 1992/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.1913 ER -