Cited By
View all- Fang ZLiu YGeng ZZhu SHe Y(2022)A local method for identifying causal relations under Markov equivalenceArtificial Intelligence10.1016/j.artint.2022.103669305(103669)Online publication date: Apr-2022
Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths
Authors: 
Yue Liu, Zheng Cai, Chunchen Liu, Zhi GengAuthors Info & Claims
Yue Liu, Zheng Cai, Chunchen Liu, Zhi GengAuthors Info & ClaimsArticle No.: 49, Pages 1 - 15
Abstract
Causal networks are used to describe and to discover causal relationships among variables and data generating mechanisms. There have been many approaches for learning a global causal network of all observed variables. In many applications, we may be interested in finding what are the effects of a specified cause variable and what are the causal paths from the cause variable to its effects. Instead of learning a global causal network, we propose several local learning approaches for finding all effects (or descendants) of the specified cause variable and the causal paths from the cause variable to some effect variable of interest. We discuss the identifiability of the effects and the causal paths from observed data and prior knowledge. For the case that the causal paths are not identifiable, our approaches try to find a path set that contains the causal paths of interest.
References
[1]
Steen A. Andersson, David Madigan, and Michael D. Perlman. 1997. A characterization of Markov equivalence classes for acyclic digraphs. Ann. Stat. 25, 2 (1997), 505--541.
[2]
David Maxwell Chickering. 2002. Learning equivalence classes of Bayesian-network structures. J. Machine Learn. Res. 2 (2002), 445--498.
[3]
Mathias Drton and Marloes H. Maathuis. 2017. Structure learning in graphical modeling. Ann. Rev. Stat. Appl. 4, 1 (2017), 365--393.
[4]
Zhi Geng, Yue Liu, Chunchen Liu, and Wang Miao. 2019. Evaluation of causal effects and local structure learning of causal networks. Ann. Rev. Stat. Appl. 6, 1 (2019).
[5]
Steffen L. Lauritzen and Thomas S. Richardson. 2002. Chain graph models and their causal interpretations. J. Royal Stat. Soc. Series B (Stat. Meth.) 64, 3 (2002), 321--361.
[6]
Marloes H. Maathuis, Markus Kalisch, and Peter Bühlmann. 2008. Estimating high-dimensional intervention effects from observational data. Ann. Stat. 37 (2008), 3133--3164.
[7]
Judea Pearl. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK.
[8]
Judea Pearl. 2001. Direct and indirect effects. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI’01). 411--420.
[9]
Peter Spirtes and Clark Glymour. 1991. An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9, 1 (1991), 62--72.
[10]
Ioannis Tsamardinos, Constantin F. Aliferis, and Alexander Statnikov. 2003. Time and sample efficient discovery of Markov blankets and direct causal relations. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 673--678.
[11]
Ioannis Tsamardinos, Laura E. Brown, and Constantin F. Aliferis. 2006. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learn. 65, 1 (2006), 31--78.
[12]
Changzhang Wang, You Zhou, Qiang Zhao, and Zhi Geng. 2014. Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach. Comput. Stat. Data Anal. 77 (2014), 252--266.
[13]
Jianxin Yin, You Zhou, Changzhang Wang, Ping He, Cheng Zheng, and Zhi Geng. 2008. Partial orientation and local structural learning of causal networks for prediction. In Proceedings of the Workshop on the Causation and Prediction Challenge at WCCI 2008 (Proceedings of Machine Learning Research), Vol. 3. PMLR, 93--105.
Index Terms
- Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths
Recommendations
Local causal network learning for finding pairs of total and direct effects
In observational studies, it is important to evaluate not only the total effect but also the direct and indirect effects of a treatment variable on a response variable. In terms of local structural learning of causal networks, we try to find all possible ...
A Survey on Causal Discovery: Theory and Practice
AbstractUnderstanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is ...
A definition and graphical representation for causality
UAI'95: Proceedings of the Eleventh conference on Uncertainty in artificial intelligenceWe present a precise definition of cause and effect in terms of a fundamental notion called unresponsiveness. Our definition is based on Savage's (1954) formulation of decision theory and departs from the traditional view of causation in that our causal ...
Comments
Information & Contributors
Information
Published In
September 2019
314 pages
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 September 2019
Accepted: 01 February 2019
Revised: 01 December 2018
Received: 01 August 2018
Published in TIST Volume 10, Issue 5
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed
Funding Sources
- 973 Program of China
- NSFC
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 295Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
Cited By
View all- Fang ZLiu YGeng ZZhu SHe Y(2022)A local method for identifying causal relations under Markov equivalenceArtificial Intelligence10.1016/j.artint.2022.103669305(103669)Online publication date: Apr-2022
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format