Yue Liu
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.
- 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.Google Scholar
Cross Ref
- David Maxwell Chickering. 2002. Learning equivalence classes of Bayesian-network structures. J. Machine Learn. Res. 2 (2002), 445--498. Google ScholarDigital Library
- Mathias Drton and Marloes H. Maathuis. 2017. Structure learning in graphical modeling. Ann. Rev. Stat. Appl. 4, 1 (2017), 365--393.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google ScholarCross Ref
- Marloes H. Maathuis, Markus Kalisch, and Peter Bühlmann. 2008. Estimating high-dimensional intervention effects from observational data. Ann. Stat. 37 (2008), 3133--3164.Google ScholarCross Ref
- Judea Pearl. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK. Google ScholarDigital Library
- Judea Pearl. 2001. Direct and indirect effects. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence (UAI’01). 411--420. Google ScholarDigital Library
- Peter Spirtes and Clark Glymour. 1991. An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9, 1 (1991), 62--72.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
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