Meaningful Causal Aggregation and Paradoxical Confounding
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 accept that macro causal relations are typically defined only with reference to the micro states. On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions. We also discuss generalizations of this observation.
Cite
Text
Zhu et al. "Meaningful Causal Aggregation and Paradoxical Confounding." Proceedings of the Third Conference on Causal Learning and Reasoning, 2024.Markdown
[Zhu et al. "Meaningful Causal Aggregation and Paradoxical Confounding." Proceedings of the Third Conference on Causal Learning and Reasoning, 2024.](https://mlanthology.org/clear/2024/zhu2024clear-meaningful/)BibTeX
@inproceedings{zhu2024clear-meaningful,
title = {{Meaningful Causal Aggregation and Paradoxical Confounding}},
author = {Zhu, Yuchen and Budhathoki, Kailash and Kübler, Jonas M. and Janzing, Dominik},
booktitle = {Proceedings of the Third Conference on Causal Learning and Reasoning},
year = {2024},
pages = {1192-1217},
volume = {236},
url = {https://mlanthology.org/clear/2024/zhu2024clear-meaningful/}
}