Invariance of Clustering Operations in Causal Effect Identification
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 o...
| Authors | Jani Nykänen, Otto Tabell, Santtu Tikka et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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