Meaningful Causal Aggregation and Paradoxical Confounding

arXiv:2304.11625v3 Announce Type: replace-cross
Abstract: In aggregated variables the impact of interventions is typically ill-defined because different micro-realizations of the same macro-intervention can result in different changes of downstream macro-variables. We show that this ill-definedness of causality on aggregated variables can turn unconfounded causal relations into confounded ones and vice versa, depending on the respective micro-realization. We argue that it is practically infeasible to only use aggregated causal systems when we are free from this ill-definedness. Instead, we need to …

abstract aggregation arxiv causality confounding cs.ai impact macro micro relations show stat.ml type variables