Identifying Causal Effects Under Functional Dependencies

We study the identification of causal effects, motivated by two improvements to identifiability that can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effec...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 26; no. 12; p. 1061
Main Authors Chen, Yizuo, Darwiche, Adnan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 06.12.2024
MDPI
Subjects
Algorithms
Analysis
causal effects
Causation
functional dependencies
Functionals
Graph theory
identifiability
Variables
Variables (Mathematics)
United States
causal effects
functional dependencies
identifiability
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Summary:We study the identification of causal effects, motivated by two improvements to identifiability that can be attained if one knows that some variables in a causal graph are functionally determined by their parents (without needing to know the specific functions). First, an unidentifiable causal effect may become identifiable when certain variables are functional. Secondly, certain functional variables can be excluded from being observed without affecting the identifiability of a causal effect, which may significantly reduce the number of needed variables in observational data. Our results are largely based on an elimination procedure that removes functional variables from a causal graph while preserving key properties in the resulting causal graph, including the identifiability of causal effects. Our treatment of functional dependencies in this context mandates a formal, systematic, and general treatment of positivity assumptions, which are prevalent in the literature on causal effect identifiability and which interact with functional dependencies, leading to another contribution of the presented work.
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This article is a revised and expanded version of a paper entitled “Identifying Causal Effects Under Functional Dependencies”, which was accepted by the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS) held at the Vancouver Convention Center.
ISSN:1099-4300
1099-4300
DOI:10.3390/e26121061