Identifying Causal Effects via Context-specific Independence Relations
Tikka, S., Hyttinen, A., & Karvanen, J. (2019). Identifying Causal Effects via Context-specific Independence Relations. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, Inc.. https://papers.nips.cc/paper/8547-identifying-causal-effects-via-context-specific-independence-relations
© 2019 Neural Information Processing Systems Foundation, Inc.
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.
PublisherNeural Information Processing Systems Foundation, Inc.
ConferenceAdvances in neural information processing systems
Is part of publicationNeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingST was supported by Academy of Finland grant 311877 (Decision analytics utilizing causal models and multiobjective optimization). AH was supported by Academy of Finland grant 295673.
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