Invariance of Clustering Operations in Causal Effect Identification

Abstract:Clustering variables in causal graphs reduces the size of the graph and simplifies causal inference. However, arbitrary clustering can alter crucial causal relations among variables and lead to erroneous conclusions. While the identifiability of a causal effect in the clustered graph implies the identifiability in the original graph under mild conditions, nonidentifiability in clustered graph does not imply nonidentifiability in the original graph without further assumptions. When both identifiability and nonidentifiability are preserved, the clustering operation is called identification invariant. We present a broad class of clustering operations that are identification invariant based on conditions related to the c-components of the original graph. Finally, we demonstrate use of the results in practical settings.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.03101 [stat.ML]

(or arXiv:2610.03101v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.03101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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