Meaningful Causal Aggregation and Paradoxical Confounding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913239119429632 |
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| author | Zhu, Yuchen Budhathoki, Kailash Kuebler, Jonas Janzing, Dominik |
| author_facet | Zhu, Yuchen Budhathoki, Kailash Kuebler, Jonas Janzing, Dominik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_11625 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Meaningful Causal Aggregation and Paradoxical Confounding Zhu, Yuchen Budhathoki, Kailash Kuebler, Jonas Janzing, Dominik Artificial Intelligence Machine Learning 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. |
| title | Meaningful Causal Aggregation and Paradoxical Confounding |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2304.11625 |