A constraint-based approach to incorporate prior knowledge in causal models

G. Borboudakis; S. Triantafillou; V. Lagani; I. Tsamardinos

A constraint-based approach to incorporate prior knowledge in causal models

G. Borboudakis, S. Triantafillou, V. Lagani, I. Tsamardinos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper we address the problem of incorporating prior knowledge, in the form of causal relations, in causal models. Prior approaches mostly consider knowledge about the presence or absence of edges in the model. We use the formalism of Maximal Ancestral Graphs (MAGs) and adapt cSAT+ to solve this problem, an algorithm for reasoning with datasets defined over different variable sets.

Original language	English (US)
Title of host publication	ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages	321-326
Number of pages	6
State	Published - Dec 1 2010
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

Cite this

@inproceedings{7cee9c4988404115a8d09f601542f290,

title = "A constraint-based approach to incorporate prior knowledge in causal models",

abstract = "In this paper we address the problem of incorporating prior knowledge, in the form of causal relations, in causal models. Prior approaches mostly consider knowledge about the presence or absence of edges in the model. We use the formalism of Maximal Ancestral Graphs (MAGs) and adapt cSAT+ to solve this problem, an algorithm for reasoning with datasets defined over different variable sets.",

author = "G. Borboudakis and S. Triantafillou and V. Lagani and I. Tsamardinos",

note = "Generated from Scopus record by KAUST IRTS on 2023-09-23",

year = "2010",

month = dec,

day = "1",

language = "English (US)",

isbn = "9782874190445",

pages = "321--326",

booktitle = "ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

}

TY - GEN

T1 - A constraint-based approach to incorporate prior knowledge in causal models

AU - Borboudakis, G.

AU - Triantafillou, S.

AU - Lagani, V.

AU - Tsamardinos, I.

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23

PY - 2010/12/1

Y1 - 2010/12/1

N2 - In this paper we address the problem of incorporating prior knowledge, in the form of causal relations, in causal models. Prior approaches mostly consider knowledge about the presence or absence of edges in the model. We use the formalism of Maximal Ancestral Graphs (MAGs) and adapt cSAT+ to solve this problem, an algorithm for reasoning with datasets defined over different variable sets.

AB - In this paper we address the problem of incorporating prior knowledge, in the form of causal relations, in causal models. Prior approaches mostly consider knowledge about the presence or absence of edges in the model. We use the formalism of Maximal Ancestral Graphs (MAGs) and adapt cSAT+ to solve this problem, an algorithm for reasoning with datasets defined over different variable sets.

UR - http://www.scopus.com/inward/record.url?scp=84887070785&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9782874190445

SP - 321

EP - 326

BT - ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

ER -

A constraint-based approach to incorporate prior knowledge in causal models

Abstract

Bibliographical note

Other files and links

Fingerprint

Cite this