Identifying Causal Effects via Context-specific Independence Relations
| Autor: | Tikka, Santtu, Hyttinen, Antti, Karvanen, Juha |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Appeared at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada |
| Databáze: | arXiv |
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