Identifying Causal Effects via Context-specific Independence Relations

• URL: http://arxiv.org/abs/2009.09768v1
• Date: Mon, 21 Sep 2020 11:38:15 GMT
• Title: Identifying Causal Effects via Context-specific Independence Relations
• Authors: Santtu Tikka, Antti Hyttinen, Juha Karvanen
• Abstract summary: Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution. We show that deciding causal effect non-identifiability is NP-hard in the presence of context-specific independence relations. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs.
• Score: 9.51801023527378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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