Identifying Causal Effects via Context-specific Independence Relations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866910509535592448 |
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| author | Tikka, Santtu Hyttinen, Antti Karvanen, Juha |
| author_facet | Tikka, Santtu Hyttinen, Antti Karvanen, Juha |
| contents | Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces context-specific independence (CSI) relations, the existing identification procedures and criteria based on do-calculus are inherently incomplete. We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs. The approach is provably sound and it includes standard do-calculus as a special case. With the approach we can obtain identifying formulas that were unobtainable previously, and demonstrate that a small number of CSI-relations may be sufficient to turn a previously non-identifiable instance to identifiable. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2009_09768 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Identifying Causal Effects via Context-specific Independence Relations Tikka, Santtu Hyttinen, Antti Karvanen, Juha Artificial Intelligence Machine Learning Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces context-specific independence (CSI) relations, the existing identification procedures and criteria based on do-calculus are inherently incomplete. We show that deciding causal effect non-identifiability is NP-hard in the presence of CSIs. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs. The approach is provably sound and it includes standard do-calculus as a special case. With the approach we can obtain identifying formulas that were unobtainable previously, and demonstrate that a small number of CSI-relations may be sufficient to turn a previously non-identifiable instance to identifiable. |
| title | Identifying Causal Effects via Context-specific Independence Relations |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2009.09768 |