Juha Karvanen

Clustering and Pruning in Causal Data Fusion

This work proposes pruning (removing unnecessary variables) and clustering (combining variables) as preprocessing operations for causal data fusion and gives sufficient conditions for inferring the identifiability or non-identifiability of a causal effect in a larger graph based on a smaller graph.