Quantifying Causal Path-Specific Importance in Structural Causal Model

Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the sco...

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Bibliographic Details
Published in:Computation 2023-07, Vol.11 (7), p.133
Main Authors: Wang, Xiaoxiao, Zhao, Minda, Meng, Fanyu, Liu, Xin, Kong, Zhaodan, Chen, Xin
Format: Article
Language:English
Subjects:
Algorithms
Analysis
causal
Causality (Physics)
Decision making
Decision theory
Gender
path-specific effect
Random variables
structural causal model
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Summary:Path-specific effect analysis is a powerful tool in causal inference. This paper provides a definition of causal counterfactual path-specific importance score for the structural causal model (SCM). Different from existing path-specific effect definitions, which focus on the population level, the score defined in this paper can quantify the impact of a decision variable on an outcome variable along a specific pathway at the individual level. Moreover, the score has many desirable properties, including following the chain rule and being consistent. Finally, this paper presents an algorithm that can leverage these properties and find the k-most important paths with the highest importance scores in a causal graph effectively.
ISSN:2079-3197
2079-3197
DOI:10.3390/computation11070133