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